Automated prediction of user response states based on traversal behavior

ABSTRACT

A game management system is configured for automated analysis of player behavior regarding traversal of different screens and options in a game application (as opposed to exclusively gameplay behavior) and to generate an automated estimation or inference about one or more psychological aspects of player behavior, such as motivation for game engagement and/or emotional states at different stages of interaction. The results of such analysis are automatically parametrized and included in a multidimensional parametric player model that informs player clustering and/or automated customization of game content.

This application is a continuation of U.S. patent application Ser. No.16/948,496, filed Sep. 21, 2020, which is incorporated herein byreference in its entirety.

BACKGROUND

Computer-implemented games are very popular. A common mechanism for theprovision of computer-implemented games, particularly on mobileelectronic devices, is by installation and execution of a client gameapplication (which comprises compiled software code) stored on a clientdevice. For some device platforms, such game applications are typicallyavailable for download and update mainly from an application store thatprovides access to games and applications from multiple differentproviders. After installation of a client game application on a clientdevice, online gameplay is typically managed and/or facilitated by agame server of a specific game provider.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, thesignificant digit or digits in a reference number refer to the figurenumber in which that element is first introduced.

FIG. 1 illustrates a system in which computer-implemented gaming may beimplemented, in accordance with some examples.

FIG. 2A is a screenshot of a game definition file code defining aruleset for a simplified example game, in accordance to some examples.

FIG. 2B is a series of screenshots showing a game event and one of aplurality of game levels forming the game event, according to someexamples.

FIG. 2C is a screenshot of level configuration file code, according toone example.

FIG. 3 data flow diagram for content generation and evaluation, inaccordance with some examples.

FIG. 4 is a data flow diagram for content generation and evaluation, inaccordance with some example.

FIG. 5 is a data flow diagram for content generation and evaluation, inaccordance with some example.

FIG. 6 is a data flow diagram for configuring a game based on a playermodel, in accordance with some examples.

FIG. 7 is a flow chart of a method for transmitting configuration valuesto a client device, in accordance with some examples.

FIG. 8 is a flow chart of a method for adjusting operation of a gameengine at a client device, in accordance with some examples.

FIG. 9 is a block diagram of a client device memory for providinginteractive gameplay, in accordance with some examples.

FIG. 10 is a flow chart of a method for providing interactive gameplay,in accordance with some examples.

FIG. 11 is a data flow diagram illustrating the generation and use ofthe multidimensional parametric player model data, according to oneexample.

FIG. 12 is a schematic flow diagram illustrating a method for generatingparametric player model data and using the player model data tocustomize game content, according to one example.

FIG. 13 is a schematic view of a system that provides a map-basedgenerator interface for automated content generation for acomputer-implemented game, according to one example.

FIG. 14A is a high-level flowchart illustrating a method to provideoperator control of automated game content generation via a generatorinterface, according to one example

FIG. 14B is a flowchart illustrating a more detailed view of a method toprovide operator control of automated game content generation via agenerator interface, according to one example.

FIG. 15A-FIG. 15C are a series of screenshots of a web-based generatorinterface for a generating custom game content, according to oneexample.

FIG. 16 is a schematic flowchart illustrating a method for automatedprocedural generation of custom game content based on player model data,according to one example.

FIG. 17 schematic data flow diagram illustrating automated proceduralgeneration of custom game content based on player model data, accordingto one example.

FIG. 18A-FIG. 18H are respective data flow diagrams for three respectiveprogressive configuration stages of a level file configuration for twoexample player models with different parametric values, according to oneexample.

FIG. 19 is a flow chart of a method for automated game assessment, inaccordance with some examples.

FIG. 20 is a flow chart of a method 2000 for automated dynamic customgame content generation, in accordance with some examples. The method2000 may be implemented at a game server.

FIG. 21 is a flowchart illustrating a method of estimating psychologicalaspects of player behavior, according to one example.

FIG. 22 is a schematic diagram of a traversal graph for a single playersession, according to example.

FIG. 23A is a schematic diagram illustrating a player journey derivedfrom a traversal graph, according to one example.

FIG. 23B is a data flow diagram of a method for building an actionembedding matrix, according to one example.

FIG. 23C is a data flow diagram illustrating the mapping of playeractions to action embedding vectors by use of an action embeddingmatrix, according to one example.

FIG. 24A is a data flow diagram for a recurrent neural networkpredicting psychological labels in a many-to-many structure, accordingto one example.

FIG. 24B is a data flow diagram for a recurrent neural networkpredicting psychological labels in a many-to-one structure, according toone example.

FIG. 25 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 26 is a block diagram illustrating components of a machine,according to some examples, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

The headings provided herein are merely for convenience and do notnecessarily affect the scope or meaning of the terms used.

DETAILED DESCRIPTION Introductory Overview

Some aspects of this disclosure provides for a game distribution andupdating mechanism in which a compiled game engine is persistentlyinstalled on a client device, while game configuration values (which arevariable to customize gameplay for different players or differentidentified groups of players) are provided to the client device in aseparate standalone data download. In some examples, the gameconfiguration values are provided to the client device as non-compileddata that defines game rules, the appearance and/or behavior ofinteractive in-game objects (also referred to herein as interactivecontent items), and configuration of respective game levels (for exampleincluding a gameboard layout, performance targets, and experience pointsand/or coin rewards for respective performance targets).

Referring briefly to FIG. 1 (which will be described at greater lengthlater), therein is shown an example of such a game provision andmanagement architecture, in which a client device 102 downloads from athird-party application store server 104 compiled game engine software106 that is then installed on the client device 102 as an executablegame engine 108. The game engine 108 is in this example a genericexecutable application that can be used, without modification, toimplement different games and/or different game variants (e.g., of thesame game) by cooperation with different non-compiled standalone gameconfiguration data 110. In some examples, the game configuration data110 may be fully or partially non-compiled. For reasons pertaining toapplication store service agreements (e.g., in which separateapplication downloads are mandated for separate games and in which asingle download is to be limited to a executing a single associatedgame), however, the game engine 108 is in some examples limited torunning only a single designated game uniquely associated with the gameengine 108. In instances where a number of different games utilizing thedisclosed techniques are played via the client device 102, a respectivegame engine 108 may be installed for each of the games, the game engines108 being functionally identical and differing only in the identity ofthe respective designated games which they are to implement.

Such game configuration data 110 is in this example separatelydownloaded to the client device 102 from a game server 112 forming partof a game management system of a game provider. The game configurationdata 110 in this example comprises separate text files or blocks of textdata (structured in predefined formats that the game engine 108 iscomplementarily programmed to identify and implement) which respectivelydefine different aspects of the game to be implemented on the clientdevice 102 via the game engine 108. Upon execution, the game engine 108in this example fetches the latest version of the game configurationdata 110 from the game server 112 and executes interactive gameplay ofthe designated game based on the attributes and parameters defined bythe game configuration data 110. In other examples, updated versions ofthe game configuration data 110 (e.g., to provide customized gameplayexperience) are pushed to the client device 102 for consumption by thegame engine 108 upon execution.

In this example, the game configuration data 110 comprises two separateinterrelated functional components, namely a game definition file 114and interactive content data 116, each of which is briefly introducedbelow and is described more extensively later herein. The gamedefinition file 114 defines gameplay rules (also referred to as gamemechanics). An example of a simplified version of such a textuallydefined ruleset 202 for a highly simplified example game (in thisinstance an elementary version of Boggle™) is shown in FIG. 2A.

The interactive content data 116 includes definitions of respectiveconfigurations, parameters, and/or attributes of interactive contentitems such as, in this example, gameboards, game pieces, gamecharacters, powerups, consumable and/or purchasable special effectitems, and the like. The interactive content data 116 in this exampleincludes multiple level configuration files 118 that respectively defineconfiguration of content items in multiple corresponding units ofgameplay, each level configuration file 118 in this example definingconfiguration values for a respective game level. As shown, the levelconfiguration files 118 are received, at the client device 102, from thegame server 112. In some cases, additional interactive content data 116,in addition to the level configuration files 118, are also received fromthe game server 112. In some cases, the interactive content data 116(e.g., the level configuration files 118 or other interactive contentdata 116) are received, at the client device 102, from a sourcedifferent from the game server 112 or are generated at the client device102.

FIG. 2B shows three screenshots that illustrate an example hierarchicalarrangement of levels in a word finding game with respect to whichvarious aspects of the disclosure will be illustrated by way of example.It is emphasized that the disclosed mechanisms and techniques canequally be applied to a variety of different game types different fromthat which is used here merely as explanatory example. The levelhierarchy in the example of FIG. 2B provides a plurality of selectivelyplayable events 204 (only one of which is shown in FIG. 2B, titled“Chapter 1”), with each event 204 comprising a sequence of game levels206 that in this example provide the smallest discrete units ofgameplay. Each game level 206 in this example has a unique gameboard 208comprising a rectangular grid of tile slots partly populated by lettertiles 210. The grid is to be navigated vertically downwards by a playercharacter 212 controlled by the player and initially occupying a top rowposition. Tile slots occupied by letter tiles 210 are collapsed byselection of a plurality of linearly adjacent letter tiles 210 togetherforming a playable word, allowing the player character 212 to dropdownwards. Other types of game object that are not shown in thegameboard 208 of FIG. 2B in this example include non-letter obstructions(see, e.g., brick tiles 1802 in FIG. 18A) and bombs that can bedetonated to destroy both letter tiles 210 and brick tiles 1802.

A level configuration file 118 in this example defines, inter alia, thesize and composition of the gameboard 208 (i.e., the identity andpositions of the letter tiles 210, the positional arrangement of bricktiles 1802), the initial position of the player character 212, andperformance targets and corresponding reward quanta (e.g., targetcompletion scores/time with respective values for in-game currencyand/or experience point rewards for satisfying the respectiveperformance targets). An example of such a structured textual levelconfiguration file 118 is shown in the extract of structured textualdata of FIG. 2C. It will be noted that the level configuration file 118includes, inter alia: a grid layout definition 214 (in which lettertiles 210 are indicated by corresponding letters, brick tiles areindicated by an “*” character, and the player character 212 is indicatedby entry “$1”); coin reward parameters 216 that define thresholds andamounts for in-game currency rewards; and points reward parameters 218that define thresholds and amounts for experience points (XP) rewards.In other examples, the level configuration file 118 may includeconfiguration and/or definition of different interactive game objects,gameboards, elements, variables, values, and the like that are differentfrom those In other examples, the level configuration file 118 mayinclude different things from those specified above with respect to thecurrent example.

It will thus be seen that the game configuration data 110 in someexamples include event configuration data, which may comprise eventtiming and level contents or reference to corresponding levelconfiguration files 118. Event configurations may include, for example:timing, and level contents. As mentioned above with reference to FIG.2C, configuration data for respective levels may include, for example: aletter grid description, a ruleset reference (e.g., in a scriptinglanguage, for example, a novel data structure for consumption by acomplementarily coded game engine 108), initial settings (e.g., amountof time allowed, etc.), and thresholds required for rewards and stars.In addition to the level configuration files 118 and the various aspectsthereof mentioned above, the game configuration data 110 in someexamples further include player configurations. The playerconfigurations may include, for example: shop parameters, login rewardparameters, experience requirements for player levels, events surfaced,and starting items.

It will be appreciated that all of these various values andconfigurations captured in the game configuration data 110 and reflectedin the designated game when it is implemented by the game engine 108 canbe fully modified, replaced, or amplified simply by modification orreplacement of the standalone game configuration data 110, without anymodification of the game engine 108. Such an architecture greatlyfacilitates customization or tuning of game variants for identifiedplayer cohorts/segments or even for players individually. In contrast toconventional application updates to game applications downloaded andinstalled from the application store server 104, effective updating ofthe designated game (including micro-targeted customization forpromoting player engagement and retainment) can be done directly fromthe game server 112 to the client device 102 by replacement ormodification of the game configuration data 110. Because the gameconfiguration data 110 is (fully or partly)non-compiled data stored onthe client device 102 in a textual file format, no installation orcompiling thereof is needed for its implementation, while bandwidth anddevice resource loads are small compared to conventional existingmechanisms for modification or reconfiguration of game applicationsinstalled on-device.

A number of further aspects of the disclosure relate to targetedcustomization of game configuration and/or interactive game content forspecific player clusters or player groups (also referred to herein asplayer cohorts). In some examples, such targeted customization includesat least partly automated generation of custom game configurationsand/or at least partly automated evaluation of such computer-generatedcontent.

Thus, e.g., in an example game such as that discussed with reference toFIG. 2A-FIG. 2C, differently configured gameboards 208 with differentperformance thresholds and letter tile layouts may be auto-generated andauto-evaluated for different respective player cohorts. Resultant customgame variants or custom interactive game content are in some examplesprovisioned or made available for players in the corresponding targetedcohort or cluster by use of the architecture described with reference toFIG. 1. In such examples, different respective targeted levelconfiguration files 118 can thus be made available for automaticdownload by the client devices 102 of players in different cohorts. Inthis manner, practically feasible targeted customization is achievableat the large scales of popular online games (in some instances havingmillions of daily active users), with relatively fine levels ofcustomization granularity achievable without incurring primitive laborcosts.

In FIG. 3, flowchart 300 shows a high-level view of an example methodfor at least partly automated targeted game customization. The examplemethod of FIG. 3 will briefly be described by way of example as beingimplemented by the example system of FIG. 1 with respect to the examplegame of FIG. 2C-FIG. 2B. At operation 302, a respective player model 126is generated or maintained for each one of multiple user accounts 128for a corresponding multiplicity of players of the game (see FIG. 1, inwhich data for the user accounts 128 is exemplified as included inplayer database 124 accessible by the game server 112).

As will be described in greater depth later herein, data indicatingplayer modeling in this example comprises a respective multidimensionalplayer model 126 maintained for each player. The player model 126 ismultidimensional in that it provides an array or set of numerical valueseffectively defining a datapoint or vector in multi-dimensional space.The multiple dimensions comprise respective pre-defined gameplaycharacteristics or attributes for the associated game based onhistorical gameplay. In the example game of FIG. 2B, respective playermodel dimensions (also referred to herein as model parameters) includeaverage length of words played, size of employed vocabulary, maximumword length, and the like. Table 1 below (discussed in the portions ofthe disclosure directed to Player Modelling) lists the respectivedimensions of a 64-dimensional player model 126 employed in the presentexample. It will be appreciated that different numbers of player modeldimensions and different respective dimensions can be employed fordifferent games.

In addition, as illustrated schematically in FIG. 1, the player database124 further includes behavior graphs 130 indicative of player behaviorwithin the game as it relates to traversing through different screens oractivities. Such game traversal behavior is to be distinguished fromgameplay attributes or characteristics as mentioned above as providingrespective dimensions of the player model 126. Such gameplay attributesor characteristics relate more narrowly to a player's style, behavior,gameplay patterns, or gameplay habits during actual gameplay. As will bedescribed at length with respect to FIG. 21, historical game traversalbehavior of players are processed to automatically generate respectivetraversal graph structures (referred to for ease of reference asbehavior graphs 130), which are automatically labelled and processedusing a trained neural network to identify estimated motivations forplayer behaviors or emotional states indicated by game interactions.Various details of such behavior graphs 130 and their use in automatedcustom content generation will be described later herein with referenceto example traversal graph 2200 shown in FIG. 22. In this example, thebehavior graphs 130 are processed to derive and populate a subset ofdimension values for the player model 126 of the corresponding player.In other examples, the behavior graphs 130 may be used separately fromthe player models 126 to inform automated content generation and/orevaluation.

Returning to FIG. 3, the method further comprises, at operation 304,identifying a target player cluster for whom game content is to becustomized. In this example, the identification or definition of atarget player cluster is based on the respective multidimensional playermodels 126, e.g. by identifying a subset of the players whose playermodel vectors are clustered together in a selected number of dimensionsof the player model 126. In this example, the player modeling ofoperation 302 and the player clustering of operation 304 are performedby a player modeling engine 120 provided by the game server 112 (FIG.1). It will be appreciated that clusters of varying degrees ofgranularity can be selected dependent on operator preference. Details ofsuch clustering will be discussed at greater length later herein. Arepresentative player model for the target player cluster is generatedfor use in content generation, e.g., defining each dimensional value ofthe representative player model as a statistical average or valuerepresentative of the respective values for that dimension of the modelvectors of the players constituting the target player cluster.

At operation 306, candidate game content for the target player clusteris automatically generated. In one example, the automated game contentthat is thus generated is a set of level configuration files 118 for asingle game event, as discussed with reference to FIG. 2B. These customlevel configuration files 118 are produced based at least in part on therepresentative player model for the target player cluster, so thatdifferent variants of the same game levels are generated for playerclusters with different representative player models 126. An example ofstepwise generation of level generation files will be described atlength below with reference to FIG. 16-FIG. 18H. In this example, theseprocedures are performed by a content generator 132 forming part of thegame server 112.

Thereafter, the auto-generated candidate game content is auto-evaluated,at operation 308. In one example, the automated evaluation comprisesforecasting a user action sequence based at least in part on theapplicable player model 126, and a resultant outcome of the forecastuser action sequence is determined.

At operation 310, candidate game content is curated, in this example inan automated procedure, based on the outcomes produced by the actionsequence forecast. In particular, this example provides for comparisonof the resultant outcome to predefined target metric(s) for the forecastgameplay. If the outcome satisfies the target metric(s), the candidategame content (e.g., a set of level configuration files 118 for aparticular game event) is, at operation 312, provisioned for thecorresponding target player cluster, e.g., by storing the custom gamecontent on the game server 112 for automatic download by respectiveclient devices 102 of players who are members of the target playercluster. If, however, the candidate game content does not satisfy thetarget metric(s), the game assessment engine 134 (FIG. 1) by whichoperation 308 through operation 312 are performed iteratively adjustsand re-evaluates the level configuration files 118 until they eithersatisfy the target metrics or are discarded.

FIG. 4 is a data flow diagram 400 for content generation and evaluation,in accordance with some examples. Respective player models 126 for theplayers are generated based on historical gameplay information(corresponding to operation 302 in FIG. 3). In addition, a database ofrespective behavior graphs 130 for the players is generated based onhistorical behavior data indicating respective players' behavior intraversal through different screens and actions within the game, asmentioned above.

In this example, the behavior graphs 130 inform automated contentgeneration by being analyzed to derive a subset of behavioral dimensionvalues that are included in the associated player model 126. In theexample of FIG. 2A-FIG. 2C, each player model 126 is made up of 64gameplay dimensions and four traversal behavior dimensions. (Note thatthe number and identity of the player model dimensions or parameters canvary widely for different examples.) Worded differently, in such anexample the player model 126 includes both dimensions relating togameplay behavior as well as dimensions relating to game traversalbehavior, also referred to some examples herein as motivationaldimensions. In other examples, behavior graphs 130 may be provided tothe content generator 132 in a manner different than throughrepresentation in the player model 126.

Based on the respective player models 126, a plurality of playerclusters are identified by a clustering operation that includescalculating distances between respective player vectors (as indicated bythe respective player models 126) in the multi-dimensional space definedfor player modeling. It will be appreciated that an operator can varythe granularity of such clusters, so that the global set of players canthus be segmented in any desired number of player clusters, which may insome cases be overlapping. In this example, a representative playermodel 126 is calculated for each player cluster, each representativeplayer model 126 representing the corresponding cluster of players,based on the corresponding underlying gameplay data. In this example,the representative player model 126 for a cluster is calculated as avector having, for each of its predefined multiple dimensions, anumerical value defined as a statistical average of the numerical valuesof the corresponding dimension of the player models 126 of all playerswho are members of the relevant cluster. Worded differently, therepresentative player model is provided by a multi-dimensional vector(or datapoint in multidimensional parameter space) whose component ineach dimension is the statistical average of the correspondingcomponents of the player vectors of all players in the cluster.

At a generator interface 402 (in this example being a web-based userinterface generator on a remote operator device communicating with thegame server 112 via the network 122), an operator triggers contentgeneration by selecting constraints and/or target metric values forcontent that is to be generated. In this example, the operator selectsor identifies via the generator interface 402: a particular playercluster (corresponding to operation 304, FIG. 3); the number of gamelevels that are to be generated/configured together to form a gameevent; respective target metric values for each level (e.g., adifficulty level for gameplay according to the representative playermodel); and an applicable ruleset 202 or game definition file 114. Therepresentative player model 126 of the selected cluster is ingested bythe generator interface 402, in this example being a hypertext markuplanguage (HTML) interface as described below with reference to FIG.15A-FIG. 15C.

The generator interface 402 communicates with content generator 132 (inthis example provided by game server 112), which generates the contentbased on the information received from the generator interface 402. Inthis example, content generation comprising production of respectivelevel configuration files 118 for each level of a game event tocustomized for the selected target cluster.

The content thus generated is provided to a simulator 404. The simulator404 simulates playing the computer-implemented game, having thegenerated content, as a player corresponding to the representativeplayer model 126 of the target cluster. The evaluator 406 evaluatesperformance metrics (e.g., win rate) based on the simulation by thesimulator 404. If the evaluated metrics meet certain criteria, they areprovided to a database 410 for consumption by respective client devices102 of players in the target cluster. Otherwise, the generated contentis adjusted by the adjuster or returned to the content generator 132 forgeneration of new content. The output of the adjuster 408 or the contentgenerator 132 is provide to the simulator 404 for further simulation.This process is performed iteratively for generated level generationlevel configuration file 118 until it is either discarded (triggeringgeneration of a new iteration of the level configuration file 118) orsatisfies the performance criteria and is stored to the database 410.

FIG. 5 is a data flow diagram 500 for game configuration valuegeneration, in accordance with some examples. As shown, a player model502 and content item(s) 504 are ingested by a game assessment engine506. The game assessment engine 506 uses the player model 502 togenerate a user action sequence 508, forecasting actions that a user,represented by the player model 502, would take in response to thecontent item(s) 504 being presented in a computer-implemented game. Thegame server 112 determines outcome(s) 510, specified in the software ofthe computer-implemented game, of the generated user action sequence508. The user action sequence 508 and the outcome(s) 510 are ingested bythe optimization engine 512. The optimization engine 512 optimizesconfiguration value(s) 514 for the content item(s) 504. The optimizedconfiguration values 514 are stored in a database 516 for consumption byclient device(s) 102 associated with the player model 502 (e.g., basedon the user account(s) of the client device(s)).

FIG. 6 is a data flow diagram 600 for configuring a game based on aplayer model, in accordance with some examples, in this example beingdescribed with reference to the example s 100 of FIG. 1. When playersplay the game using respective client devices 102, gameplay data 602 isgenerated. In addition, game traversal behavior (e.g., user behavior inmoving through different screens and actions within the game, incontradistinction to gameplay behavior that relates to actual gameplay)is recorded and processed to generate behavior graphs 130 (introduced inFIG. 1 and described at length with reference to FIG. 21) stored ingraph database 608. The gameplay data 602 and the information stored inthe graph database 608 are used to generate a player model 126representative of behavior of a cluster cohort of players. As describedelsewhere, the representative player model 126 is in some examplescompiled by creating a respective multidimensional parametric playermodel 126 for each player, identifying player clusters by identifyingclusters of datapoints indicated by respective individual player models126 in a multidimensional parameter space defined by predefined playermodel parameters, and generating a representative player model 126 basedon the constituent player models 126 of an identified target cluster. Inother examples, an individual player model 126 is identified asrepresentative of a target player cluster, and is used for gameconfiguration specific to the target player cluster.

The player model 126 maps to configuration value(s) 604 for the game.The configuration value(s) 604 may be determined for the player model126 using the techniques described throughout this document (e.g., withone or more of the multiple player model parameters mapping one-to-oneor in respective combination to a plurality of different configurationsand features of a level configuration file 118 (FIG. 2C). After theconfiguration value(s) 604 are determined, they are stored in a database606 for ingestion by client devices 102 corresponding to the playermodel 126.

DETAILED DESCRIPTION Example System Architecture

In view of the above introductory overview, some aspects of thedisclosure will now be discussed at greater length. As used herein, agame includes a piece of entertainment that one or more players engagewith according to particular objectives. For each game there isarticulated a set of rules that govern how the players are allowed tomodulate some representation of the current state of play over time, andthe consequences of that modulation. A game can be uniquely described byits rules along with its Initial Game State.

A game state may refer to a snapshot of the game at a given point intime. The location of the pieces in Chess along with whose turn it is tomake the next move would be a representation of its game state. For somecomputer-implemented games, this Game State is significantly morecomplex, capturing things like the current score, player's currentattributes (health, ammunition etc.) and so forth. A Game State fullydescribes all aspects that are relevant to gameplay at a given moment.An Initial Game State provides the Game State when the game starts, andis the configuration from which players first engage with the game.

A game mechanic is the term given to a set of one or more game rulesthat act together to create a specific behavior within the game. Forexample in Chess, a “mechanic” might be based around a single piece, theway that it moves, captures opposing pieces and any special rules forthat piece such as the Rook's “castling” or the Pawn's “en passant”capture ability. New pieces could be added to Chess by adding theirrelevant rules as a group of mechanics.

Game Configuration Values are the set of data (e.g., game parameters)that encapsulates the behavior of the game. They include a GameDefinition File and Interactive Content Items that describe a variety ofaspects of the game and encapsulating software product that can beconfigured in a variety of ways to provide distinct experiences toplayers.

A Game Definition File provides a complete enumeration of the rules of agame, in some example below also referred to as a ruleset. Each ruleincludes what must be true for the rule to be able to take effect andwhat the consequence of the rule is. Some rules describe actions takenby the player (e.g. moving a piece in Chess) while others describe rulesthat are evaluated when the Game State changes for any reason (e.g.being in ‘Check’ in Chess).

Interactive Content Items are the sets of items with which playersinteract. They may describe specific levels of a Game, typically bydescribing a specific Initial Game State. They also govern aspects suchas how those levels are surfaced to players as part of Events, as wellas a range of settings for a player such as what items will be visibleto them in the “shop” built into the software, or rewards that a playerwill receive for logging into the game each day.

A player model may in some examples include a representation of a real,hypothetical, or representative player in multi-dimensional space,reflecting values against a number of parameters or metrics that definecharacteristics of play. In some examples, all parameters/metrics arenormalized to contain values between 0 and 1. A single Player Model is avector in this dimensional space, and in the case that it represents areal player, it is calculated based on their observed historical play. APlayer Model can also be representative of a group of players (e.g., acluster of vectors in the parameter space), where the vector is theblended average of the datapoints for each member of the group.

A player modeling engine may include an engine (implemented in software,hardware or a combination of software and hardware) for calculatingPlayer Models based on analysis of historical data and clustering ofsimilar players. In some examples, the player modeling engine (e.g., theplayer modeling engine 120) additional estimates aspects of playerpsychological features based on machine-learned label prediction, asdescribed with reference to the example method of FIG. 21.

A Player Journey may refer to the “path” that a given player has takento navigate through a piece of software, also referred to as gametraversal. The “journey” is their transition from viewing one area ofthe software to another, and might capture the menu screens that aretraversed or the levels that a player plays in a game. The utility ofthe Player Journey lies in its ability to facilitate an understanding ofwhat a player is putting their attention on within the game, with theaim of understanding their motivations and aspirations. See in thisregard again the description with reference to FIG. 21.

FIG. 1 illustrates a system in which the various aspects pertaining tocreation, management, support, and provision of computer-implementedgaming may be implemented, in accordance with some examples. As shown,the system includes an application store server 104, a game server 112,a player database 124 (which may be implemented as a graph database),and a client device 102 connected to one another over a network 122. Thenetwork 122 may include one or more of the Internet, an intranet, alocal area network, a wide area network, a wired network, a wirelessnetwork, and the like. The client device 102 may be a smartphone, atablet computer, a laptop computer, a desktop computer and the like. Itshould be noted that the player database 124 is referred to as a“database” in this document. However, the player database 124 is notlimited to any particular structure or storage format of data, and maybe implemented as a data repository or a data storage unit that is not adatabase.

The application store server 104 may implement an application store thatprovides software to multiple client devices, including the clientdevice 102. For example, the application store server 104 may beassociated with the Apple App Store®, or the Google Play Store®. Asshown, the application store server 104 stores the game engine software106, which is provided to the client device 102.

The game server 112 generates and stores information associated withgameplay of computer-implemented game(s). As shown, the game server 112hosts a player modeling engine 120, which develops player models 126(which may be multi-dimensional) for storage in the player database 124.Each player model is associated with one or multiple user accounts 128stored in the player database 124. In some cases, a player model 126corresponds to a cluster of user accounts 128 in a multi-dimensionalspace, which each dimension representing a different gameplay element orparameter. Each user account 128 is associated with a user who plays thecomputer-implemented game(s).

The player models 126 are used to generate game configuration data 110,including the game definition file 114 and the interactive content data116 (including the level configuration files 118) for the game. The gameconfiguration data 110 may represent a game mechanics configuration or agame parameter configuration. The game configuration data 110 mayrepresent a player configuration, an event configuration, a levelconfiguration or a scoring configuration.

The player models 126 may be used to generate a game definition file114. The game definition file 114 stores rules for thecomputer-implemented game. The game definition file 114 includes ruleswritten in non-compiled machine-readable code.

The player models 126 are used to generate interactive content data 116customized to the individual player models, in order to increase userengagement with the computer-implemented game. The interactive contentdata 116 may include game boards, game pieces, game characters, and thelike.

The game server 112 generates a user action forecast, forecasting userinteraction with the interactive content data 116 based on the playermodel 126. Metrics (e.g., a win rate, a gameplay time, and the like) arecomputed, by the game server 112, based on the user action forecast.

The interactive content data 116 may include one or more of playerconfigurations, event configurations, and levels. Player configurationsmay include: shop parameters, login reward parameters, experiencerequirements for player levels, events surfaced, and starting items.Event configurations may include: timing, and level contents. Levels mayinclude: a letter grid description, a ruleset reference (e.g., in ascripting language, for example, Lex-Script), initial settings (e.g.,amount of time allowed, etc.), and thresholds required for rewards andstars.

As shown, all or part of the game configuration data 110, which includethe game definition file 114 and the interactive content data 116, aretransmitted from the game server 112 to the client device 102. The gameengine 108 of the client device 102 receives, from the application storeserver 104, the game engine software 106. The game engine 108 accessesthe received game configuration data 110 (including the game definitionfile 114 and the interactive content data 116) during execution of thegame engine 108. The execution of the game engine 108 at the clientdevice 102 corresponds to gameplay of computer-implemented game(s).

During gameplay at the client device 102, which includes execution ofthe game engine 108, data about the gameplay of the user account 128associated with the client device 102 are stored in the player database124. These data are provided to the player modeling engine 120 of thegame server 112 to generate a player model 126 for the user account 128of the client device 102 (or to associate the user account 128 of theclient device 102 with a previously existing player model 126).

Some examples relate to the game engine 108 for use at the client device102. The client device stores the game engine 108 for playing adesignated game. In some cases, the game engine 108 is also for playingadditional games, in addition to the designated game. In some cases, thegame engine 108 is for playing the designated game only. The clientdevice 102 receives, over the network 122, game configuration data 110that specify operation of the designated game. The game configurationdata 110 represent a game mechanics configuration or a game parameterconfiguration. The client device 102 adjusts operation of the gameengine 108 based on the received game configuration data 110, withoutmodifying software of the game engine 108. In some examples, the gameengine software 106 (which may include all of the compiled code for thegame engine 108) is received entirely from the application store server104, and not from the game server 112.

According to some examples, the game server 112 receives, from theplayer database 124, which communicates with the client device 102, dataassociated with gameplay of one or more user accounts 128, the one ormore user accounts being used for playing a designated game. The gameserver 112 computes the player model 126 representing previous in-gamebehavior of the one or more user accounts. The game server 112identifies, based on the player model 126, game configuration data 110for the designated game. The game configuration data 110 represent datato specify operation of the designated game. The game configuration data110 represent at least one of a game mechanics configuration and a gameparameter configuration. The game server 112 causes transmission of thegame configuration data 110 to the client device 102. The gameconfiguration data 110 cause the client device 102 to adjust the gameengine 108 based on the previous in-game behavior of the one or moreuser accounts 128.

According to some examples, the client device 102 stores the game engine108 for playing a designated game. The client device 102 receives, overthe network 122, game configuration data 110 for the designated game.The game configuration data 110 represent data to specify operation ofthe designated game. The client device 102 adjusts operation of the gameengine 108 based on the received game configuration data 110 withoutmodifying the game engine software 106 of the game engine 108.

Some examples relate to automated game assessment. In some examples, theplayer model 126 may include a set of numbers, e.g., providingrespective parametric values. The game server 112 accesses the playermodel 126 representing a subset of players. The player model 126 isgenerated based on previous in-game behavior of the subset of playerswhile playing a computer-implemented game. The game server 112 accessesa set of interactive content data 116 associated with the game. The gameserver 112 forecasts, using the player model 126, a sequence of useractions (e.g., the user action forecast) of the subset of players duringgameplay of the game. The sequence of user actions represents aprediction of user interaction with the set of interactive content data116. The game server 112 computes, based on the forecasted sequence ofuser actions and software-defined outcomes of the forecasted sequence ofuser actions in the game, game configuration data 110 for the set ofinteractive content data 116. The game server 112 causes execution ofgameplay at the client device 102 associated with the player model 126.The gameplay is according to the computed game configuration data 110for the set of interactive content data 116.

Some examples relate to fully automated dynamic custom game contentgeneration. The game server 112 generates a set of game content items(e.g., interactive content data 116) for a designated game. The set ofgame content items is customized for one or more user accounts 128 basedon a player model 126 (generated by the player modeling engine 120)representing the one or more user accounts 128. The player model isgenerated based on previous in-game behavior of the one or more useraccounts while playing the designated game. The game server 112forecasts, using the player model 126, a sequence of user actions (e.g.,the user action forecast) of the one or more user accounts duringgameplay of the designated game with the generated set of game contentitems. The sequence of user actions represents a prediction of in-gameuser interaction with the set of game content items. The game server 112computes, based on the forecasted sequence of user actions andsoftware-defined outcomes of the forecasted sequence of user actions inthe designated game, a set of metrics associated with gameplay of theone or more user accounts represented by the player model in thedesignated game. The game server 112 determines whether the set ofmetrics corresponds to a predefined range (e.g., the win rate is between50% and 70%). Upon determining that the set of metrics corresponds tothe predefined range: the game server 112 stores the game configurationdata 110 for transmission to the client device 102 associated with theplayer model 126. Upon determining that the set of metrics does notcorrespond to the predefined range: the game server 112 may adjust theset of content items until the set of metrics enters the predefinedranges or a stopping criterion is met.

Some examples relate to a scripting language for the game definitionfile 114. A machine-readable medium, such as a memory unit of the clientdevice 102, stores the game definition file 114 for a designated game.The game definition file 114 stores rules for the designated game. Thegame definition file defines the rules using non-compiledmachine-readable code (e.g., scripting code in a scripting language, forexample, Lex-Script). The machine-readable medium also stores compiledsoftware code for the game engine 108. The game engine is for playingmultiple games including the designated game via a graphical userinterface (GUI) according to the rules for the designated game definedby the game definition file.

According to some examples, the client device 102 receives (e.g., fromthe application store server 104) compiled software for the game engine108 (e.g., game engine software 106). The game engine is for playingmultiple games, including the designated game. The client device 102receives (e.g., from the game server 112) the game definition file 114.The game definition file 114 stores rules for the designated game. Thegame definition file 114 defines the rules using non-compiledmachine-readable code. The client device 102 executes the game engine108 to enable playing of the designated game at the client device 102.To enable playing of the designated game, the client device 102 accessesthe game definition file 114 and provides, via a user interfacegenerated on the client device 102, interactive gameplay of thedesignated game according to the rules for the designated game definedby the game definition file 114.

In one example, a game definition file 114 may include a “toggle select”rule, which requires that a block being selected by the user is adjacentto the block that was last selected. A “submit” rule may verify that aword that the user is trying to submit has not been previouslysubmitted. Another rule may specify that tiles that are selected areremoved from the game board.

Some examples relate to a workflow for creating game content, evaluatingthe content, and ensuring that it meets certain criteria. The newcontent may be placed into the broader context of an event with unlockcriteria, rewards, and so on. The game server 112 generates content thatis pre-tuned for a set of users based on the player models 126.

FIG. 7 is a flow chart of a method 700 for transmitting configurationvalues to a client device, in accordance with some examples. The method700 may be implemented at a game server. In the example of FIG. 1, themethod 800 is implemented by game server 112.

At operation 702, the game server receives data associated with gameplayof one or more user accounts. The one or more user accounts are used forplaying a designated game. The data associated with the gameplay may bereceived from client device(s) or from a graph database storing historicgameplay data.

At operation 704, the game server computes, for the one or more useraccounts, a player model representing previous in-game behavior of theone or more user accounts. The previous in-game behavior of the one ormore user accounts includes playing the designated game.

At operation 706, the game server identifies, based on the player model,configuration (config) values for the designated game. The configurationvalues represent data to specify operation of the designated game. Theconfiguration values may represent one or more of: a game mechanicsconfiguration, a game parameter configuration, a player configuration(e.g., data related to a player in the game), an event configuration(e.g., data related to an event in the game), a level configuration(e.g., data related to a level of the game), and a scoring configuration(e.g., data related to scoring the game). The player model may includemultiple dimensions. The game server identifies, based on the playermodel, the configuration values for the designated game by clusteringthe player model into a group of player models using statisticalmethods. Each group of player models corresponds to its own associatedconfiguration values.

At operation 708, the game server causes transmission of theconfiguration values to a client device. The configuration values causethe client device to adjust a game engine at the client device based onthe previous in-game behavior of the one or more user accounts. Theconfiguration values are stored separately from the game engine and donot adjust the software of the game engine. The software of the gameengine may have been received from an application store server, whichmight be different from the game server. In some cases, theconfiguration values are transmitted to the client device in astandalone data payload over the air. The standalone data payload istransmitted directly from the game server to the client device, withoutpassing through the application store server. The game engine is usedfor playing different game(s), including the designated game, at theclient device.

The client device stores the game engine. The game engine isconfigurable using the configuration values. The configuration values donot modify software of the game engine and are stored externally to thesoftware of the game engine.

FIG. 8 is a flow chart of a method 800 for adjusting operation of a gameengine at a client device, in accordance with some examples. The method800 may be implemented at the client device.

At operation 802, the client device stores, in memory, a game engine forplaying a designated game and, in some cases, also additional games.Software for the game engine may be received over a network from anapplication store server.

At operation 804, the client device receives, over the network,configuration (config) values for the designated game. The configurationvalues represent data to specify operation of the designated game. Insome cases, the configuration values are received, at the client device,in a standalone data payload over the air. The standalone data payloadis transmitted directly from the game server to the client device,without passing through an application store server. The configurationvalues may be stored, in the memory of the client device, externally tothe game engine.

At operation 806, the client device adjusts operation of the game enginebased on the received configuration values, without modifying thesoftware of the game engine.

According to some examples, the separation of the configuration valuesfrom the software of the game engine allows the game developer to updatethe game at client device(s) by controlling the game server, withoutfrequently accessing the application store server. Software for the gameengine may be updated, via the application store server, for example,once every several months (or any other time period) and the softwaremay be the same for all client devices having the same operating system(e.g., iOS or Android). The configuration values, on the other hand, maybe updated multiple times per day (or any other time period) and may beselected, by the game developer, based on the player model associatedwith a given user account. Thus, the designated game may be implementeddifferently for users with different gameplay styles, and the designatedgame may be modified as the user's gameplay style changes. For example,in a farming game, a user who likes cows may be given a cow as a rewardfor reaching a certain level. Another user, who prefers growingvegetables, may be given a carrot instead of a cow. In a modified chessgame, a player who is associated with a player model that indicatesinterest in larger board sizes may play at a 16×16 (rather than thetypical 8×8) board. A player who is associated with a player model thatindicates interest in novel pieces may be offered to play with one ormore novel pieces. For example, a lieutenant piece, which can move 4squares forward or to the right, or 3 squares backward or to the left,may be added to the board.

Game Rules Definition Files

FIG. 9 is a block diagram of a client device memory 900 for providinginteractive gameplay, in accordance with some examples. As show-n, thememory 900 stores a game engine 906 and a game definition file 902externally to the game engine 906. The software for the game engine 906and the game definition file 902 may be received from different sources,but may be used for gameplay of a common designated game.

As shown, the game definition file 902 for the designated game storesgame rules 904 for the designated game. The game definition file 902defines the game rules 904 using non-compiled machine-readable code. Thegame engine 906 is for playing the designated game (and, in some cases,also other game(s)). The game engine enables 906 interactive gameplay ofthe designated game via a graphical user interface (GUI) according tothe game rules 904 for the designated game defined by the gamedefinition file 902.

In some cases, the game definition file 902 represents a formal firstorder predicate logic for playing the designated game via the gameengine 906. At least one of the game rules 904 may include a logicalmanipulation of a game state in response to a user action or a currentgame state. The logical manipulation of the game state may indicate howa character or a content item is moved in the designated game or howpoints are assigned in the designated game.

FIG. 10 is a flow chart of a method 1000 for providing interactivegameplay, in accordance with some examples. The method 1000 may beimplemented at a client device.

At operation 1002, the client device receives compiled software for agame engine. The game engine is for playing the designated game and, insome cases, also other game(s). The compiled software for the game playengine may be received, at the client device, from an application storeserver.

At operation 1004, the client device receives a game definition file forthe designated game. The game definition file stores rules for thedesignated game. The game definition file defines the rules usingnon-compiled machine-readable code. The game definition file may bereceived, at the client device, from a game server. The applicationstore server may be separate and distinct from the game server.Alternatively, at least a portion of the application store server may beidentical to at least a portion of the game server.

At operation 1006, the client device executing the game engine to enableplaying of the designated game via the client device. As illustrated,executing the game engine includes operations 1008 and 1010. Atoperation 1008, the client device accesses the game definition file. Atoperation 1010, the client device provides a graphical user interface(GUI) to enable interactive gameplay of the designated game according tothe rules for the designated game defined by the game definition file.The interactive gameplay may include providing, via the GUI, a graphicaloutput and receiving, via the GUI at the client device, a user input fortaking a user action in the designated game.

In some examples, the client device receives an updated game definitionfile for the designated game. In some examples, the game definition filemay be updated very frequently (e.g., once or multiple times per day)while the game engine is updated much more rarely (e.g., once permonth). This technique allows the game developer to make frequentupdates to the game using the game definition file, without needing toaccess the application store server or update the game engine. In somecases, different game rules may be provided to different client devicesto enable different gameplay experiences at the different clientdevices. This may be used, for example, to tailor a user's gameplayexperience to his/her interests or to A/B test potential new features.The client device executes the game engine at the client device to playthe specified game by: accessing the updated game definition file andproviding a GUI to enable interactive gameplay of the designated gameaccording to the rules for the designated game defined by the updatedgame definition file. The updated game definition file may be receivedwithout receiving an updated game engine and without updating the gameengine.

Multidimensional Parametric Player Modeling

As introduced above with reference to the example of FIG. 1-FIG. 6 (andexpanded on briefly in FIG. 3 and FIG. 6), one aspect of the disclosurerelates to methods, systems, and techniques for generating amultidimensional parametric player model representative of playerbehavior by one or more players in a computer game. The parametricplayer model thus populated is used in the identification of groups orclusters of players, and/or in at least partly automated configurationof custom game content for behavior consistent with the parametricplayer model. A single parametric player model (that is, a single set ofparametric values corresponding to multiple predefined gameplayparameters) can be used to model the behavior of a single respectiveplayer (e.g., based on historical gameplay data 602 of that player) orto model the behavior of multiple players (e.g., based on cumulativehistorical gameplay data 602 for the relevant players), providing arepresentative player model for those players.

Various features and functionalities according to this aspect of thedisclosure are described below by way of example with reference to FIG.11-FIG. 12, which is for clarity and ease of reference described asbeing implemented by s 100 described previously with reference toFIG. 1. Similar system components are referenced by similar referencenumbers in the examples of FIG. 12-FIG. 13, on the one hand, and theremainder of the figures, on the other hand. Note that, although variousaspects relating to the player modeling are described in the examplethat follow as being incorporated in a system that employs, incombination, the various other aspects of this disclosure, the disclosedparametric player modeling techniques can in other examples be employedseparately from at least some of the other techniques discussed herein.Thus, for example, multidimensional parametric player modeling can insome examples be used for identifying player segments or clusters basedon clustering the respective player models. In other examples,parametric player modeling and partly automated game customization basedthereon can be employed without provisioning the resultant customizedgame in an architecture comprising an on-device compiled game engineconsuming non-compiled game configuration data. Note that theabove-exemplified alternative incorporation of the automated contentgeneration in broader game management architectures and methods is notexhaustive.

Referring briefly to FIG. 3 and the associated description above, itwill be noted that the disclosure provides for generation and use of aparametric player model that represents gameplay behavior as a set ofparametric values corresponding to a parameter set defining multiplerespective gameplay parameters (interchangeably referred to herein asdimensions of a parameter space defined by the parameter set). Notethat, in some examples, the player model may include not only parameterspertaining to gameplay, but additionally include one or more traversalparameters indicative of a game traversal behavior or journey (e.g., auser's non-gameplay behavior, style, action patterns, habits, orinferred motivations as it relates to moving through different screens,actions, or options within the game in one or more sessions).

The example described below with reference to FIG. 11 and FIG. 12employs a parameter set limited to gameplay parameters, but it should beappreciated that other examples include in the player model a subset oftraversal parameters. In the examples discussed previously withreference to FIG. 1-FIG. 6, as well as the examples described below withreference to FIG. 21, a player modeling engine 120 calculates a numberof traversal parameters from behavior graphs 130 derived from gametraversal data 610, and includes the calculated numerical values forthese traversal parameters in the relevant player model. In someexamples, the traversal parameters indicate parametric values for one ormore psychological features associated with gameplay behavior (e.g., aplayer's emotional state or motivation for engagement), and are thusalso referred to herein as psychological parameters or dimensions of theplayer model. The techniques described below with reference to gameplayparameters and modeling are thus to be understood as extending byanalogy to modeling and use of traversal or psychological parameters andmodeling. Thus, the techniques and mechanisms described as relating togameplay parameters can in other examples be performed, in addition toor separately from, any number of traversal parameters. Therefore, aseparate parametric traversal model can in some examples be generatedand employed in automated assessment and content generation. In theexample described below, the parameter set consists of 64 gameplayparameters/dimensions. In another example that employs generation andprocessing of behavior graphs 130 for the same game, the player modelincludes these 64 gameplay parameters and an additional four traversalparameters/dimensions.

Turning now to FIG. 11, therein is shown a data flow diagram 1100schematically illustrating some aspects of data production and flow inthe s 100 (FIG. 1, see also FIG. 6) according to an example method 1200represented schematically in the flow diagram of FIG. 12. At operation1202 (FIG. 12), the player modeling engine 120 accesses historicalgameplay data 602 for a superset of players consisting of all of theplayers with respective user accounts 128 (FIG. 1).

In this example, the gameplay data 602 is on an ongoing basis generatedand maintained by the player modeling engine 120 based on gameplay ofthe multiple players via their respective client devices 102. Thegameplay data 602 in this example is generated by analyzing gameplayinputs and calculating respective parametric values for each of apredefined structured parameter set. Each parametric value is anumerical value that represents player behavior for a corresponding oneof the gameplay parameters. In this example, each parametric value is areal number, meaning that it is a floating-point number allowing fordecimal values. In this regard, note that in examples where original rawparametric values are retained in the player model without statisticalmanipulation (such as by normalization), the parametric values for someof the gameplay parameters (e.g., a maximum word length) can forindividual player models 126 necessarily be limited to natural numbers,but that the parametric values for such parameters in representative orcomposite player models (which are constructed based on the gameplaydata 602 for multiple players) can nevertheless have a value located ona number line between adjacent natural numbers (i.e., having decimals).

All possible combination of values for the parameter set can bevisualized as a conceptual multidimensional parameter space in which theprincipal dimensional axes of an n-dimensional Cartesian coordinatesystem are defined by the parameter set. Thus, in the example playermodel structure described below with reference to Table 1, the parameterset defines a 64-dimensional space whose dimensions are defined by thelisted game parameters. For this reason, the gameplay parameters of theparameter set are occasionally also referred to herein as dimensions ofthe player model. Because each gameplay parameter in the parameter setessentially has a continuous range of possible values, themultidimensional parameter space is in effect a continuous space.

Any particular player model 126 (i.e., a single set of parametric valuesmapping one-to-one to the parameters of the parameter set) therefore beinterpreted as providing coordinates indicating a particular discretedatapoint in the multidimensional space, analogously to theidentification of any point in physical space by three dimensionalvalues for its x-y-z coordinates. A straight line in the parameter spacebetween such a datapoint and the origin of the coordinate axes thusdescribes a vector in the multidimensional space. For this reason, asingle set of parametric values for the parameter set is variouslyreferred to herein synonymously as a player model, a datapoint in theparameter space, or a gameplay vector.

In the example previously described with reference to FIG. 1-FIG. 3, theparameter set for the game of FIG. 2A-FIG. 2C is defined as listed inTable 1 below.

ORDINAL GAMEPLAY SLOT CATEGORY PARAMETER DEFINITION 1 vocabulary in-gamevocabulary Total number of distinct per level words spelled 2 vocabularyIn-game vocabulary [Total number of distinct per level wordsspelled]/[total levels played] 3 vocabulary total words spelled Totalnumber of words spelled 4 vocabulary word frequency [total wordsspelled]/[in- game vocabulary] 5 vocabulary avg word length Averagelength of words spelled 6 vocabulary max word length The maximum lengthof words spelled 7 vocabulary stdev word length Standard deviation forlength of words spelled 8 vocabulary avg word spelled Average timeinterval speed between each word spelled 9 vocabulary avg word scoreAverage word score without multiplier without including multipliers 10vocabulary avg word score Average score of each word spelled 11vocabulary stdev word scores Standard deviation for without multiplierwhich scores without including multipliers 12 powerupcnt_powerup_usage - Count of times hints hint were used 13 powerupcnt_powerup_usage - Count of times bombs bomb were used 14 powerupcnt_powerup_usage - Count of times score score multiplier multiplierswere used 15 powerup cnt_powerup_usage Count of times big big bomb bombswere used 16 powerup cnt_powerup_usage - Count of times letter letterbag bags were used 17 powerup total_scores - hints Total scores earnedby using hints 18 powerup total_score - bombs Total score earned byusing bombs 19 powerup total_scores - score Total score earned bymultiplier using score multipliers 20 powerup total_scores - big Totalscore earned by bombs using big bombs 21 powerup avg_time_remaining-Average time remaining first powerup used in the level when the pairused the first powerup 22 powerup avg_time_remaining- Average timeremaining first hints used in the level when the player used the firsthints 23 powerup avg_time_remaining- Average time remaining firstbombsused in the level when the player used the first bombs 24 powerupavg_time_remaining- Average time remaining first score multiplier in thelevel when the used player used the first multiplier 25 powerupavg_time_remaining- Average time remaining first big bombs used in thelevel when the player used the first big bombs 26 powerupavg_time_remaining- average time remaining first letter bags used in thelevel when the player used the first letter bags powerup 27 store totalspend on Total amount spent on purchse - hints purchasing hints 28 storetotal spend on Total amount spent on purchse - bombs purchasing bones 29store total spend on Total amount spent on purchse - score purchasingscore multiplier multipliers 30 store total spend on Total amount spenton purchse - big bombs purchasing big bombs 31 store total spend on Theamount spent on purchse - letter bags purchasing letter bags 32 storetotal count of Count of hints purchased purchase - hints 33 store totalcount of Count of bombs purchase - bombs purchased 34 store total countof Count of score purchase - score multipliers purchased multiplier 35store total count of Count of big bombs purchase - big bombs purchased36 store total count of Count of letter bags purchase - letter bagspurchased 37 store total amount Total amount of hints purchsed -hintspurchased 38 store total amount Total amount of bombs purchsed - bombspurchased 39 store total amount Total amount of score purchsed - scoremultipliers purchased multiplier 40 store total amount Total amount ofbig purchsed- big bombs bombs purchased 41 store total amount Totalamount of letter purchsed- letter bags bags purchased 42 store # storerefresh Count of store refreshes 43 store total spend on store Totalamount spent on refresh store refreshes 44 store purchase on Total spendon discounted items discounted items 45 performance avg time remainingAverage time remaining per level at level completion 46 performancevertical move per Average number of rows word spelled moved down byplayer character for each word spelled 47 performance daily total winsAverage total of wins per day 48 performance daily total loss Averagetotal losses per day 49 performance daily win/loss ratio Average dailywin-loss ratio 50 performance daily total quits Average number of timesper day the player exits game level before it ends 51 performance avgscore per level Average score per game level completed 52 performanceavg stars per level Average number of rewards stars earned per gamelevel 53 performance avg leaderboard rank Average rent for the player ongame-wide leaderboard 54 performance max level unlocked Highest gamelevel unlocked by game progress 55 engagement avg replay number of timesthe player replays the level that they've passed before (to achievehigher score or get more rewards) 56 engagement daily time Average timespent on playing the game per day 57 engagement daily total levelsAverage number of played levels played per day 58 engagement dailylevel-up speed Average daily level-up speed 59 engagement avg experienceAverage increase per day gained per day in XP 61 wealth daily minwallet - Average daily minimum coins coin balance 62 engagement dailymax wallet - Average daily maximum coins coin balance 63 engagementdaily spend - coins Average daily coins spent 64 engagement dailysession Average number of gameplay sessions per day

In this example, the parametric values for these parameters arenormalized. e.g., being normalized to unity and having a value rangingbetween zero and one indicative of a prevalence of the underlying rawvalue. Thus, a daily total wins value of 0.5 indicates that the relevantplayer (or set of players) display average performance for thisparameter, while lower values indicate worse performance and highervalues indicate better performance. It will be appreciated thatdifferent mathematical or statistical normalization or equalizationmechanisms can be employed in different examples to provide a commonvalue range for the entire parameter set.

At operation 1204 (FIG. 12), the player modeling engine 120 on anongoing basis automatically populates respective individual playermodels 126 for each of the multiple players in the global player set. Inthe present example, having the parameter set of Table 1, the playermodeling engine 120 thus analyzes the gameplay data 602 for each playerand populates a respective parametric value for each of the 64 gameplayparameters. Each player model 126 is in this example stored as astructured series of 64 separated parametric values, with the ordinalposition of each value indicating its mapping to a corresponding one ofthe gameplay parameters as per the ordinal slot indicated in Table 1.These sets of values are stored in the player database 124 as individualplayer model data 1102 (FIG. 11).

At operation 1206 (FIG. 12), the player modeling engine 120 performs aclustering operation that processes the individual player model data1102 to identify one or more player cohorts or segments formingrespective subsets of the global set of players by identifying clustersof vectors or datapoints in the multidimensional parameter spacerepresented by the respective individual player models 126. Playercluster data 1104 (FIG. 11) thus produced is stored in the playerdatabase 124. The player cluster data 1104 in this example includesrespective identifiers (e.g., numerical labels) for a plurality ofidentified clusters together with identification of the particularsubset of players included in each cluster. This enablesoperator-selection of any one or more of the clusters for analysis orcustom content generation (e.g., via a cluster selection mechanism 1524provided in a web-based generator interface 402, as described withreference to FIG. 15C below). It will be appreciated that there arewell-established techniques for such mathematical analysis ofmultidimensional geometry and calculating relationships and arrangementsof points in multidimensional space. Different clustering techniques canbe employed in different examples.

In this example, the player modeling engine 120 automatically calculatesa distance (i.e., the length of the shortest rectilinear spacing in themultidimensional parameter space) between respective datapointsindicated by the individual player models 126 in the parameter space.Clusters are then identified based on proximity as indicated by thecalculated interstitial distances. In this example, the clusteringoperation can be controlled or directed by an operator such that thegranularity of clustering can be selectively varied. Thus, for example,the operator can select to segment the entire parameter space into 2clusters, 10 clusters, 100 clusters, or any desired number of clusters.In a particular example, cluster identification is performedhierarchically, by identifying a number of primary clusters (e.g., 10clusters) from the original global player set, identifying a number ofsecondary clusters (e.g., 10 clusters) within each primary cluster, andidentifying a number of tertiary clusters (e.g., 10 clusters) withineach secondary cluster.

It will thus be seen that the disclosed modeling clustering techniquesallows wide flexibility in identifying different clusters of players forcustomization or analysis. Moreover, different selective clusteringtechniques can be employed in other examples. In this example, playerclustering is performed in the full 64-dimensional parameter space, butin other examples a subset of the full parameter set can be selected forsegregated clustering, so that clusters of datapoints are identified ina multidimensional subset parameter space defined by the selected subsetof parameters.

At operation 1208, a representative player model 126 is compiled for oneor more of the identified player clusters, producing representativeplayer model data 1106 stored in the player database 124 for use duringcontent generation and/or automated evaluation. Each representativeplayer model 126 is generated based on the respective individual playermodels 126 of the players included in the relevant player cluster. Inthis example, the representative player model 126 is compiled bycalculating a respective parametric value for each of the predefined setof gameplay parameters based on the respective corresponding parametricvalues of the constituent individual player models 126 of the cluster atissue. In this example, each parametric value is calculated as theaverage of the parametric values of the constituent player models 126.This technique is illustrated below by way of a highly simplifiedabbreviated example for a player cluster with three members:

Cluster 7:

-   -   Player A Player Model: [0.7; 0.4; . . . ; 0.1; 0.8]    -   Player B Player Model: [0.8; 0.9; . . . ; 0.5; 0.1]    -   Player C Player Model: [0.2; 0.6; . . . ; 0.8; 0.4]        Representative player model for Cluster 7: [0.56; 0.63; . . . ;        0.47; 0.43]

Note that different techniques for calculating a statistical average fora statistically representative parametric value for each of theparameters can be used in other examples. Note also that some examples(e.g., in which player cluster identification is otherwise than bycomputing clusters in multidimensional space, e.g., by operatorselection of a pre-defined cluster or by prior player segmentation oncriteria other than the multidimensional parameter space) can providefor calculation of a representative data model for an identified orselected player cluster or cohort directly from the gameplay data 602,without compiling respective individual player models 126. Differenttechniques for providing a representative player model 126 for a playercluster can be used in other examples. In one such alternative example,a most representative one of the individual player models 126 in thecluster (e.g., a centroid vector in the parameter space) is identifiedand selected as representative player model 126 for use inauto-configuration of custom game content for the player cluster.

When game content is to be customized for one or more player clusters,the representative player model 126 for each player cluster is accessedand consumed by the content generator 132 and the evaluator 406 (FIG.11, as well as previous discussions with respect to FIG. 1-FIG. 6),using the representative player model 126 as target player model 1108(FIG. 11) for at least partly automated customization. Note that theoperations that follow is described as being performed with respect to asingle player cluster, but can in some examples be repeatedautomatically (e.g., in a batch processed triggered by an operator) foreach one of a plurality of target clusters for which different gamecontent is to be customized or custom generated.

Thus, at operation 1210, the content generator 132 performs automatedgeneration of configuration data for a unit of gameplay (in thisexample, generating a level configuration file 118 as described atgreater length with reference to FIG. 16-FIG. 18H) based at least inpart on the applicable target player model 1108, thus having aconfiguration targeted or customized with respect to gameplay behaviorof the target cluster as represented by the target player model 1108.Thus, different variants of identical game levels or game events willhave different configurations resulting directly from differences intheir respective target player models 1108.

At operation 1212, candidate level configuration files 1718 thusauto-generated by the content generator 132 are evaluated, at operation1212, in an automated operation by the evaluator 406. As described atgreater length with reference to FIG. 20, such auto-evaluation includessimulating, at operation 1216, gameplay of each level configuration file118 based on the applicable target player model 1108. In the presentexample, simulation of gameplay at operation 1216 comprises forecastinga sequence of gameplay actions consistent with the parametric values ofthe target player model 1108, so that the automated gameplay forecastwould typically have different outcomes for an identical levelconfiguration file 118 forecast according to different target playermodels 1108. At operation 1218, one or more performance metrics for theoutcomes of the simulated gameplay are compared to respectivecorresponding target metrics predefined for the respective levelconfiguration files 118. In one example, the target metrics define adifficulty level (e.g., quantified as a win-loss probability) for eachlevel configuration file 118. In the example described below withreference to FIG. 13-FIG. 15C, respective target difficulty values areset consistent with operator input via the web-based generator interface402.

If, at operation 1218, the evaluator 406 determines that a particularcandidate level configuration file 1718 fails to satisfy predefinedinclusion criteria with respect to the target metric(s) (e.g., fallingwithin a predefined range of the specified target difficulty value), thecandidate level configuration file 1718 is adjusted, and is reevaluatedby simulated play. This adjustment and evaluation cycle is performediteratively until the candidate level configuration file 1718 satisfiesthe inclusion criteria, or is discarded. If, at operation 1218, theevaluator 406 determines that the level configuration file 118 satisfiesthe predefined inclusion criteria, the level configuration file 118 isaccepted, included in the relevant game event, and stored forprovisioning, at operation 1214, exclusively to players who are membersof the relevant target player cluster.

In summary, it will be seen from FIG. 11 that the individual playermodel data 1102 is used in identifying player clusters and constructingrepresentative player model data 1106 for the identified clusters. Atarget player model 1108 representative of a cluster of players is inturn used by the content generator 132 in the auto-configuration of gamecontent specifically for the target cluster, and is used by theevaluator 406 for targeted forecasting of gameplay and gaming outcomesspecifically according to gameplay behavior modeled by the target playermodel 1108.

Benefits of the disclosed player modeling methods and systems include inthat it enables automated identification of groups of players based onsimilar behavior, and can do so at widely varying levels of granularityat minimal cost in terms of operator time and effort. Moreover, theparametric player model circumvents existing biases or conventionalnotions of player grouping, so that different playing styles or personasare an emergent property of the player modeling and clustering. In thismanner, deeper insights with respect to player behavior can be gleanedthan previously possible.

Furthermore, the player model provides a mathematical representation ofrespective playing styles or behaviors, facilitating ingestion of theplayer model information by a content generator to inform automatedcontent generation, as disclosed in some examples herein.

Generator Interface

Another aspect of the disclosure relates to the provision of a generatorinterface that enables an operator to trigger automated generation by acontent generator of a game event based on operator selection via thegenerator interface of respective values for a target metric (e.g., adifficulty level) for each of a plurality of game levels that aretogether to form the game event. In addition, the generator interface insome examples enables the operator to selectively vary the particularnumber of game levels that are together to form the game event, via anevent sizing mechanism provided by the generator interface. Instead, orin addition, the generator interface in some examples provides a clusterselection mechanism enabling operator-selection of a particular one of aplurality of predefined subset of players (such player segments orcohorts in some examples being identified based on clustering in amultidimensional parameter space, and thus referred to for the purposesof this description as player clusters), with the game event beingcustom-generated for the selected player cluster based on arepresentative multidimensional parametric player model specific to theselected player cluster.

Various features and functionalities according to this aspect of thedisclosure are described below by way of example with reference to FIG.13-FIG. 15C. As can be seen with reference to FIG. 13, the example thatfollow is for clarity of description and ease of reference described asbeing implemented by a system 1300 analogous in architecture to s 100described previously with reference to FIG. 1. Similar system componentsare referenced by similar reference numbers in the examples of FIG. 1and FIG. 13, while some system components from FIG. 1 and fromdescriptions of other example systems and system components herein areomitted for brevity in the following description pertaining tofunctionalities of the generator interface, but these system componentsare to be read as tacitly included in the system 1300 of FIG. 13, unlessotherwise indicated or unless the context clearly indicates otherwise.The system 1300 thus includes a game server 112 configured to manage theprovision and support of a computer-implemented game playable viarespective client devices 102 at least intermittently connected to thegame server 112 over a distributed computer network 122 (in this examplethe Internet). As described previously with reference to FIG. 2A-FIG.2C, the game server 112 in this example maintains game configurationdata 110 in a data structure in which multiple game events each has aplurality of respective game levels, with the configuration of each gamelevel being defined by a respective level configuration file 118. Notethat different examples can employ different structured arrangements forgame configuration data, without departing from the scope of thisdisclosure. Moreover, consistent with targeted customization of gamecontent as described elsewhere herein, the game configuration data 110in this example comprises, for at least some game levels, a number ofdifferent variants of each level configuration file 118, with differentvariants having different configurations customized for differentrespective subsets of players (referred to below as player clusters).

As described elsewhere herein, at least some of the level configurationfiles 118 is in this example auto-generated and/or auto-curated based onplayer model data that models player gameplay behavior asmultidimensional vectors or datapoints in a high-dimensional parameterspace in which each one of multiple predefined gameplay parametersprovide respective dimensions of the parameter space. In this example, aplayer database 124 includes respective player models 126 compiled foreach player of the game based on historical gameplay behavior. Storedplayer model data in this example further includes a respectiverepresentative player model 126 for each of a plurality or multiplicityof predefined or pre-identified clusters. In this example, at least someplayer clusters are defined or identified based on proximity orclustering of their respective player model datapoints in themultidimensional parameter space.

A representative player model 126 is in this example generated for acorresponding player cluster by calculating a statisticallyrepresentative value (e.g., an average value, a median value, a centroidvalue, or the like) for each parametric dimension of the player modelbased on the corresponding dimensional values of the constituent membersof the player cluster. In another example, one of the constituent playermodels (i.e., the player-specific multidimensional parametric datapoint)that is most representative of the relevant player cluster (e.g., acluster centroid vector) is selected as a representative player model126. Such representative player models 126 is in this example calculatedupon definition or identification of the respective player clusters,which representative player models 126 are retrievable for subsequentuse in game customization or content generation. In other examples, arepresentative player model 126 can be calculated on the fly during gamecustomization or content generation for the respective player cluster.

The game server 112 further includes a content generator 132 configuredfor performing automated configuration of game content, in this examplebeing configured for automated generation and curation of a plurality oflevel configuration files 118 for a game event such that thecorresponding plurality of game levels are customized for gameplayaccording to the dimensional values of a particular player model 126 forwhich targeted customization is specified by an operator. To this end,the game server 112 includes an interface manager 1304 configured toprovide and manage control of the content generator 132 by an operatorvia a remote operator device 1302 in communication with the game server12 via the network 122. As will be seen in the description that follows,the generator interface 402 allows the input of operator commands andcontrol parameters for flexibly variable generation of game eventscustomized to selectable player clusters, with communication between thegenerator interface and the content generator 132 being facilitated bythe interface manager 1304.

The generator interface 402 in this example a web-based interfacegenerated in communication with the interface manager 1304 within abrowser application 1306 executing on the operator device 1302. In thisexample, the browser application 1306 provides an HTML interface, but itwill be appreciated that other browsing standards can be employed inother examples.

Various functionalities and input mechanisms of the generator interface402 according to one example will now be described with reference toexample methods 1400 (represented schematically in a high-level exampleflow diagram in FIG. 14A, and in a more detailed example low-level flowdiagram in FIG. 14B) at the hand of the example screenshots shown inFIG. 15A-FIG. 15C.

At operation 1402, method 1400 causes display on the operator device1302 of the generator interface 402 with respect the content generator132, which is configured for at least partly automated generation ofgame configuration data 110 for a game event in the computer-implementedgame, the game event comprising a plurality of game levels. FIG. 15Ashows a home screen 1502, in which the operator can select an eventgeneration option 1504 that causes display of an event generator screen1506 (FIG. 15C) that provides a number of input mechanisms fortriggering automated generation of custom game content by the contentgenerator 132.

The method 1400 further comprises, at operation 1404, receiving via anevent sizing mechanism 1508 provided by the generator interface 402,operator input of an event size value that quantifies the plurality ofgame levels which are together to form the game event and for whichrespective level configuration files 118 are to be generated. In thisexample, the event sizing mechanism 1508 comprises a text input boxshowing a current value for the number of game levels that are to begenerated for the new event. This event size value can be changed eitherby typing into the text box, or by stepwise incrementation ordecrementation using respectively a plus button or a minus buttonforming part of the event sizing mechanism 1508. In the example shown inFIG. 15C, the new event is to be generated with 10 game levels.

At operation 1406, operator input is received via a target inputmechanism 1510 that defines, for each of the game levels, a respectivevalue for a target metric with respect to gameplay performance in thatgame level. The target metric is in this example a difficulty level ofthe respective game levels, which are individually variable via thetarget input mechanism 1510. As shown in the screenshot of FIG. 15C, thetarget input mechanism 1510 1 in this example defines a target curve1512 that visually represents the respective selected difficulty valuesfor respective game levels. The value for each game level is representedby a node 1514, which in this example is separately manipulable by theoperator using a click-and-drag input (or alternatively a mousewheel orkeyboard arrow input).

At operation 1408, operator input is received that causes automatedgeneration by the content generator 132 of game configuration data 110for the game event such that the game event has the specified number ofgame levels, each of which is auto-evaluated to have a difficulty levelcorresponding to the respective target value received via the targetinput mechanism 1510. In this example, such automated content generationis triggered by operator-selection of a generator trigger 1516 providedby a soft button labeled “Generate a New Event”, causing the generatorinterface 402 to communicate to the content generator 132 the relevantinput parameters or seed constraints for automated generation of thegame event.

In the example of FIG. 14A, the method 1400 describes a high-levelexample in which a new game event is generated for a global set ofplayers (i.e., not for a particular subset of the players) and isgenerated for gameplay according to game rules applicable universallythroughout the game. FIG. 14B, however, schematically illustrates a moredetailed example in which control functionalities of the generatorinterface 402 is employed to cause the generation of game content thatis customized for a particular subset or cluster of players based ontheir gameplay behavior or style, and in which the game rules applicableto the newly generated game event (e.g., the applicable game definitionfile 114 as per the example of FIG. 1) is selectively variable by theoperator.

Thus, when the generator interface 402 is displayed, at operation 1402,the operator is presented with an event list 1518 that indicates a listof existing predefined game events that can be selected for triggeringgeneration of one or more customized variants thereof. Each entry in theevent list 1518 includes metadata of the game event, includingidentification of a particular predefined ruleset that defines arespective set of game rules or game mechanics (see, e.g., elementaryruleset 202 in FIG. 2A) that can be selected for governing gameplay in anew game event or a new variant of a game event that is to be generated.If the user selects a code editing option 1520 on the home screen 1502(e.g., to create a new ruleset for selection in the generation of newgame event), the generator interface 402 displays, at operation 1410(FIG. 14B), a code editing mechanism 1522 (FIG. 15B) that displays theselected ruleset as editable code in an editing window within thegenerator interface 402. The editing window enables the operator toedit, at operation 1412, the code of the selected ruleset, which theoperator can then save such that the edited ruleset is available forselection so as to be applicable to current or future game events to begenerated via the generator interface 402.

When the event generator screen 1506 is displayed responsive touser-selection of the event generation option 1504, the operatorselects, at operation 1414, from the list of predefined rulesets (inthis example, a list of predefined rules definition files 114) aparticular ruleset that is to apply to the game event to be customizedor generated. This is done via a rules selection mechanism 1526 (FIG.15C) provided by the generator interface 402 in this example in the formof a drop-down UI element.

At operation 1416, the operator selects, via a cluster selectionmechanism 1524, a particular one of a predefined list of player cohortsor player clusters for which the game event is to be customized. As willbe understood from the description elsewhere herein, such customizationprovides that the game event (and therefore of the individual gamelevels) are configured based specifically on modeled gameplay behaviorrepresentative of historical gameplay behavior of the players in theselected cluster. Thus, in this example, the game event will beconfigured based on a representative multidimensional parametric playermodel 126 specific to the selected player cluster. Thus, the generatorinterface 402 in this example retrieves the pre-compiled player model126 for the selected player cluster, at operation 1418, for inclusionthereof in input values or seed constraints eventually passed to thecontent generator 132. It will be appreciated that this data retrievaloperation can in other examples be performed at a different stagesubsequent to player cluster selection, and can in other examples beperformed by components other than the generator interface 402, e.g., bythe interface manager 1304.

At operation 1404, the event size value is received via the event sizingmechanism 1508, as described previously. At operation 1420 (whichcorresponds to operation 1406 in the flowchart of FIG. 14A), therespective target metric values for the selected number of game levelsare defined by operator-controlled configuration of the target curve1512 via the target input mechanism 1510.

At operation 1422, (responsive to operator selection of the generatortrigger 1516) input values or seed constraints based on the operatorinputs are passed to the content generator 132 for auto-configuration ofthe novel game event. In this example, the input values communicated tothe content generator 132 includes: the selected ruleset or gamedefinition file 114; the representative player model 126 for theselected player cluster; the selected number of game levels constitutingthe game event; and the target metric values for the respective gamelevels. Note that the user-selected values for the target metric (here,difficulty level) is in this example label values, but that thecorresponding target metric values communicated to the content generator132 are respective numerical values derived from the selected labelsaccording to a pre-defined schema or conversion algorithm.

Following completion of the method 1400, a game event having thespecified number of level configuration files 118 for respective gamelevels is generated by the content generator 132 such that an estimateddifficulty of the game levels for gameplay consistent with the gameplaybehavior of the representative player model 126 conforms to theoperator-selected target curve 1512 with respect to level difficulty.These, mechanisms are described at length elsewhere herein.

Automated Content Generation

As introduced previously, one aspect of the disclosure relates tomethods, systems, and techniques for automated generation of gamecontent based on a plurality of parametric values that model gameplaybehavior of one or more players for whom the game content is to becustomized. The disclosure provides for generating such custom gamecontent, performance of a content generation procedure that comprises apredefined sequence of progressive configuration stages in whichconfiguration data for a unit of gameplay (e.g., a game level) isprogressively populated with custom values determined at eachconfiguration stage for respective configurable features basedparametric values for one or more associated parameters from aparametric player model for a targeted player or set of players.

Various features and functionalities according to this aspect of thedisclosure are described below by way of examples with reference to FIG.16-FIG. 18H, which is for clarity and ease of reference described asbeing implemented by example s 100 described previously with referenceto FIG. 1-FIG. 6. Similar system components are referenced by similarreference numbers in the examples of FIG. 16-FIG. 18H, on the one hand,and the remainder of the figures, on the other hand. Note that, althoughvarious aspects relating to the custom game content generation areillustrated in the description that follows by way of example as beingincorporated in a common example system that employs, in combination,the various other aspects of this disclosure, the disclosed game contentgeneration techniques can in other examples be employed separately fromat least some of the other aspects of the disclosure. Thus, for example,the custom content generation procedures can in some examples beemployed without provisioning the resultant customized game in anarchitecture comprising an on-device compiled game engine consumingnon-compiled game configuration data. In other examples, the contentgenerator can consume as inputs for customization gameplay parametersderived directly from parsing gameplay data, without formal compilationand consumption of a player model that stores multiple parametric valuesin a predefined structured series. Similarly, the operation of a contentgenerator for automatically generating custom content can in someexamples be triggered and/or directed by a control mechanism that doesnot provide the beneficial functionalities of the web-based operatorinterface as disclosed. Note that the above-exemplified alternativeincorporation of the automated content generation in broader gamemanagement architectures and methods is not exhaustive.

This aspect of the disclosure provides a content generator configured togenerate new content (or in some examples to re-configure content, whichis also included within the meaning of content generation, thegeneration of configuration data, or the like as used herein) using aprocedural content generation technique. This technique can best beunderstood and will be discussed with reference to the elementaryexample game of FIG. 2A-FIG. 2C, in which each game level has a uniqueletter tile gameboard 208 with normally impassable brick tilesobstructing some vertical paths for a player character 212 to reach alast row in the gameboard 208 and thus complete the game level. Suchgame levels have a number of configurable features that can be variedfrom one game level to another to change game experience or difficultylevel, while operating according to the same basic game mechanics orrule set. Configurable features in this example includes (but is notlimited to) the height of the gameboard 208 (i.e., the number of rowsmaking up the gameboard); game over conditions (whether the game istime-limited or move-limited, for example); the composition of thegameboard 208 (i.e., the spatial arrangement of different types oftiles, determined by setting the tile type for each slot in thegameboard); the starting position of the player character 212: theletters that are included in the gameboard 208 on respective lettertile; requirements for earning awards and coins; and a definition ofother content items that appear in the game level.

To avoid repetition, the example content generation procedure describedbelow at the hand of FIG. 17-FIG. 18H provides for generating a customlevel definition file for a specific target player model by settingcustom values for three configurable features, namely gameboard height,gameboard composition, and letter arrangement and distribution. Notethat the term “value” as it relates to configurable features is notlimited to a single numerical or alphanumerical value. Instead, a set ofparameters or populated data fields together defining configuration of aconfigurable feature is understood as a single custom value. Thus, a setof identifiers specifying the type or specifying the letter tileidentity of each tile in the gameboard is considered a custom value forthe respective configurable feature.

Some existing techniques for computer-assisted content generationgenerates values for the respective configurable features randomly, withthe resultant auto-generated candidate game levels thereafter beingcurated for suitability. The disclosed procedure, however, is informedby parametric player model information such that content isautomatically generated specifically for the type of gameplay indicatedby the model (and by extension for the type of player or cluster ofplayers whose gameplay behavior is represented by player modelinformation).

For example, in the case of a player that tends to act quickly in shortstaccato bursts, content generation based on the player modelinformation that parametrizes this tendency can automatically providerelatively shorter game levels, relatively shorter time allotmentcompletion, or the like. In this manner, the disclosed techniquesenables a player model—representing either a single player or theblended average (e.g., cluster centroid) of a group of players asdescribed above—to influence configuration of the content that isgenerated. Such content generation is described as being customized, inthat its configuration is tailored to a specified gameplay style ortype, as represented by the player model. Worded differently, generationof game levels for the same game event but based on divergent playermodels automatically results in correspondingly divergent game levelconfigurations for the respective player models.

In this example, the automated customization of the game level (e.g.,automated generation of a level configuration file 118 having a customconfiguration) is at least partly randomized, in that at least some ofthe configurable values are informed by the player model is determinednot by a deterministic relationship between player model value(s) andcustom configuration value(s), but by biasing or constraining arandomized selection of the respective custom value(s). Thus, forexample, a particular parametric value of the player model can in suchexamples result in randomized determination of a correspondingconfiguration value being biased towards a variable seed valuedetermined based on the particular parametric value. Instead, or inaddition, the configuration value can in some examples be constrained toa value range that is variable based on player model values.

The disclosed automated content generation procedure moreover providesfor the determination of custom values for a plurality of configurablefeatures in a predefined sequence of stages building one upon the other,with the configuration data for the game level being progressivelypopulated with additional custom values at each sequential stage.Moreover, the custom values of at least some of the stages serve asconstraints or input values upon which the determination of customvalues in subsequent stages are based. For this reason, the particularsequence of stages are predefined so as to leverage the progressivelayering of a predefined data structure (e.g., a level configurationfile or databus with initial default values or null values for theconfigurable features to produce a customized level configuration file118). Such a sequence of configuration stages can be visualized as apipe with each configuration stage providing a step in a series of stepsalong the pipe. Each step (configuration stage) in this examplemodulates or customizes the value of one respective configurable featureor aspect of a predefined data structure (indicated in some of thedrawings as a data bus labeled PipeDataBus) until the final datastructure (e.g., a level configuration file 118 or custom values for thepredefined configurable features enabling finalization of a candidatelevel configuration file 118 by incorporation of the custom valuestherein). Each pipe step thus receives a current version of the databus, incorporates a new custom value for a respective one of theconfigurable features, and thus produces an updated current version ofthe data bus consumed by the immediately subsequent pipe step. In someexamples, one or more of the configuration stages synchronouslycustomizes more than one configurable feature.

FIG. 16 shows a flow diagram for a method 1600 for automatedconfiguration of a game event having a plurality of game levels for theexample game of FIG. 2A-FIG. 2C. The method 1600 is described as beingperformed by the content generator 132 of s 100 introduced in FIG. 1based on a parametric player model 126 having the respective dimensionalparameters of Table 1 above. FIG. 17 shows an overview data flow diagramfor generating custom configuration values for a single game levelaccording to the method 1600, while FIG. 18A-FIG. 18H show respectiveflow diagrams for the constituent stages of a three-step contentgeneration procedure performed consistent with FIG. 16 and FIG. 17 fortwo players with different respective player models 126, indicated inFIG. 18A and FIG. 18H as Player 1 and Player 2 respectively (e.g., beingrepresentative player models 126 for respective player clusters). Thus,the player models 126 of Player 1 and Player 2 is used as target playermodels for the respective clusters.

At operation 1602, the content generator 132 accesses the applicabletarget player model 126. As described above, each target player modelcomprises a full set of parametric values for a predefined parameterspace associated with the relevant game. The content generator 132 inthis example retrieves or receives the respective parametric values fora predefined subset of the dimensional parameters, particularly thoseparameters that are consumed in any of the configuration stages.

At operation 1604, the content generator 132 commences the contentgeneration procedure for a particular one of the game levels, with thesubsequent operations being repeated for each of the specified number ofgame levels that are together to form the game event. In other examples,the generation procedure can be performed separately for each gamelevel. In this simplified example, the configuration pipe has treesequential configuration stages, each stage populating a level data bus1716 (FIG. 17) with a custom value for a respective configurablefeature, so that a current version of the level data bus 1716 isprogressively layered with one custom value upon the other. Inparticular:

-   -   Stage 1 (indicated by reference numeral 1702 in FIG. 17)        determines the number of rows that is to constitute the        gameboard, as illustrated schematically in FIG. 18A;    -   Stage 2 (reference numeral 1704, FIG. 17) determines the layout        of a gameboard 208 having the number of rows determined in Stage        1 (see FIG. 18E); and    -   Stage 3 (reference numeral 1706. FIG. 17) determines respective        letter values for each of the letter tiles for a gameboard        having the custom row value from Stage 1 and the custom layout        from Stage 2 (See FIG. 18H).

Thus, at operation 1606 (FIG. 16) is first configuration stage iscommenced, with the immediately following series of operations beingrepeated for each of the predefined number of stages in the particularpipe. At operation 1608, a current version of the level configurationdata structure is received. See in this regard the respectivecompositions the example level data bus 1716 in the respective flowdiagrams of FIG. 18A-FIG. 18H, labeled in these diagrams as PipeDataBus.As can be seen in FIG. 17, Stage 1 thus receives the original default ornull level data bus 1716 a and updates it with a custom row height value(see operation 1616, FIG. 16) creating an updated version of the leveldata bus 1716 b, which is in turn received by Stage 2.

At operation 1610, the parametric value of the data model for apredefined one of the model's parameter associated with thatconfiguration stage is retrieved. This associated or mapped parameter isreferred to herein as the linked parameter for the relevantconfiguration stage. The parametric value for the linked parameter isindicated in FIG. 17 as a data model value 1708. Note that the differentstages in this example have different linked parameters that informtheir determination of the respective custom value.

At operation 1612, the content generator derives a seed value 1712 ortransform parameter (labeled in FIG. 18A-FIG. 18H as TransormParams) isderived based on the value of the linked parameter of the target datamodel based using a predefined respective response function 1710. Notethat in some examples, such a transform operation may be omitted, withthe value of the relevant parameter being used directly as a seed valuefor biasing randomized value determination or input value fordeterministic calculation of the relevant custom value. In this example,a respective single variable response function 1710 for each stageexpresses the seed value as a function of the linked parameter.Specifics of two contrasting examples illustrating differences in customconfiguration for the different example player models are discussedbelow with reference to FIG. 18B and FIG. 18D.

At operation 1614, the content generator 132 determines the custom valuefor the respective configurable feature based on the seed value. Thus,here Stage 1 determines a row value for the height of the gameboardbased on a seed value that is derived by a respective response function.As mentioned, the seed value in this example indicates a bias value forbiased random generation of the row value. In the present example, theseed value indicates a value between 0 and 1 around which randomlygenerated values in multiple iterations are probabilistically to becentered. It will be appreciated that such biasing of random valuegeneration is well-established and that any suitable mathematical orstatistical mechanism for biased or constrained minimization can beemployed.

At operation 1616, the newly generated custom value of the relevantconfigurable feature is incorporated in the level data bus 1716 databus,progressively layering the databus to finally output a candidate levelconfiguration file 1718, at operation 1618. Thereafter, the candidatelevel configuration file 1718 is in this example auto-evaluated,adjusted, and reevaluated (at operation 1620) until it is eitherdiscarded or accepted. Finally, a custom game event with respectivecustom-configured level configuration files is output, at operation1622.

This example of FIG. 18A-FIG. 18H illustrate the effect of therespective configuration stages to create differentiated content for twodifferent players using the same generator workflow. At each stage, therespective flow diagram shows how the game level is progressivelyconstructed, indicating the respective configurable value customized ateach stage. Progressive population of the level data structure(PipeDataBus) is shown in these respective data flow diagrams.

In this example, the content generation procedure ingests three linkedparameters with normalized parametric values for the respectiveconfiguration stages. Practical implementations will typically have agreater number of linked parameters. The linked parameters herecomprise:

-   -   avg word length: tracks the average word length this person uses        relative to the whole audience. Player 1 has a value of 0.3, so        typically uses shorter words. Player 2 has a value of 0.5, so is        about average.    -   Daily total wins: Number of typical wins for each player per        day, again normalized relative to the audience. Player 1        typically looks for more wins in a single day than Player 2,        which informs how the level is constructed to provide such an        experience.    -   Count powerup usage-bomb: This metric tracks the usage of a bomb        powerup. The bomb destroys tiles, making the level easier to        complete, so that indication by the respective parametric value        that Player 1 tends to use this powerup and Player 2 does not        automatically cause the content generator to tailor the level to        suit their divergent playstyles.

FIG. 18B and FIG. 18D illustrates usage of a response functionmechanism, as discussed above. The response curve system serves to takerelevant parameters from the player model and transform them intosuitable inputs for determining the configuration of the game level. Inthis way, utility theory techniques are employed to decouple theparticular parametric values of any one instance of the player model,and implement a more dynamic approach.

For simplicity, a single gameplay dimension (daily total wins) is usedin the example of FIG. 18B to determine the seed value, but, asmentioned, other examples include response functions that defines theseed value as a function of a plurality of gameplay dimensions. Theresponse parameter here uses a data description of a mathematicalfunction and the relevant linked parameter to derive the seed value(TransformParams) used by the content generator 132 to determine therelevant custom configuration value.

In the example of FIG. 18B, the illustrated response function pertainsto Stage 2, in which the daily total win metric informs selection of apredefined difficulty-labeled set pieces that, when selected, togethercompose the gameboard layout. Player 1 has a relatively high daily totalwin metric. Player 1's value of 0.7 becomes a seed value(TransformParam) of 0.84 which, is the used to bias randomized selectionfrom the predefined set pieces. The seed value of 0.84 is reasonablyhigh, so the inclusion of relatively more complex set pieces (as pertheir pre-assigned difficulty values).

As shown in FIG. 18D, Player 2 by contrast has a relatively low value of0.3 for their daily total wins parameter. The same response function asin FIG. 18B now generates a final value of 0.19 for the seed valuepassed to the Stage 2 customization. This results in biasing towardseasier set pieces. In the example of FIG. 18H, this leads to theselection of a set piece that reduces the width of the level by addingimpassable stone tiles on the left of the screen, making verticalpassage of the player character much easier than is the case for thelayout generated for Player 1.

In similar manner, it will be noted that in Stage 1 (in which theaverage word length informs gameboard height customization), Player 2'sa higher value for the average word length (0.5 vs 0.3) results in ataller gameboard (22 rows vs 19 rows), correlating with greaterdifficulty.

Likewise, note that in Stage 3 (in which the powerup bomb usage informsletter population), Player 1's higher parametric value (0.7 versus 0.2,indicating more regular bomb usage) results in the inclusion in thegameboard for Player 1 of less frequently occurring letters, such as theletters K and Y.

From the above examples it will be evident that a benefit the proceduralcontent generation procedure using parametric player model inputs togenerate customized content for target players or groups of player isthat it greatly facilitates the provision of satisfactory gamingexperiences across a wide variety of player styles. Significantly, thisis achieved with greater flexibility and greater sensitivity to gameplayvariations than existing techniques, yet does so while enablingsignificantly greater atomization and consequent lower cost thanexisting systems.

Automated Game Assessment

FIG. 19 is a flow chart of a method 1900 for automated game assessment,in accordance with some examples. The method 1900 may be implemented ata game server.

At operation 1902, the game server accesses a player model representinga subset of players. The player model is generated based on previousin-game behavior of the subset of players while playing acomputer-implemented game. According to some examples, each player ofthe subset of players represented by the player model corresponds to adata point in a multiple dimensional space. The player model correspondsto a data point at a centroid of the subset of players in the multipledimensional space. The dimensions of the multiple dimensional space maycorrespond to information that is determined based on observing thegameplay of the players.

At operation 1904, the game server accesses a set of interactive contentitems associated with the game.

At operation 1906, the game server forecasts, using the player model, asequence of user actions of the subset of players during gameplay of thegame. The sequence of user actions represents a prediction of userinteraction with the set of interactive content items. According to someexamples, to implement the forecasting, the game server presents, to theplayer model, set of the content in an order in which the set ofinteractive content items is presented during gameplay of the game. Thisis akin to the game server “playing the game” as a player represented bythe player model. In some examples, the set of interactive content itemsprompts the sequence of user actions in the game.

According to some examples, the forecasting leverages a statisticalengine. The statistical engine may use any statistical modeling orartificial neural network-based modeling. For example, the statisticalengine may include a utility response curve or at least one artificialneural network.

At operation 1908, the game server computes, based on the forecastedsequence of user actions and software-defined outcomes of the forecastedsequence of user actions in the game, configuration values for the setof interactive content items. Computing the configuration values mayinclude optimizing the configuration values based on metric(s). Themetric(s) may include one or more of: a target win rate, a repeatgameplay metric, a gameplay duration, a game engagement metric, and arevenue metric. Alternatively, any other metric(s) may be used. In oneexample, the metric(s) include the target win rate being 60% or between50% and 70%, as this win rate causes high user interest and engagementwith the computer-implemented game. (In some examples, a lower win ratemay be considered too challenging and non-rewarding, and a higher winrate may be considered not challenging enough.)

At operation 1910, the game server causes execution of gameplay at aclient device associated with the player model. The gameplay isaccording to the computed configuration values for the set ofinteractive content items.

Automated Dynamic Custom Game Content Generation

At operation 2002, the game server generates a set of game content itemsfor a designated game. The set of game content items is customized forone or more user accounts based on numerical values from a player modelrepresenting the one or more user accounts. The player model isgenerated based on previous in-game behavior of the one or more useraccounts while playing the designated game. The set of game contentitems may include interactive content items prompting the user actionduring gameplay of the designated game. The set of game content itemsmay be generated based on at least the player model and previouslyexisting game content items of the designated game. For example, ifusers associated with the player model interact more with game pieceshaving Type A than with game pieces having Type B, the set of gamecontent items may include additional game pieces of Type A or additionalgame pieces similar to Type A. The set of game content items may beassociated with a new level of the designated game.

At operation 2004, the game server forecasts, using the player model, asequence of (one or more) user actions of the one or more user accountsduring gameplay of the designated game with the generated set of gamecontent items. The sequence of user actions represents a prediction ofin-game user interaction with the set of game content items.

At operation 2006, the game server computes, based on the forecastedsequence of user actions and software-defined outcomes of the forecastedsequence of user actions in the designated game, a set of metricsassociated with gameplay of the one or more user accounts represented bythe player model in the designated game. The game server provides anoutput based on the set of metrics, as described below. Alternatively,any other type of output (e.g., printing or transmitting the set ofmetrics or another value derived based on the set of metrics) may beused. The set of metrics may include at least one of: a user engagementmetric, a gameplay metric, and a revenue metric. The gameplay metric mayinclude a minimum win rate (e.g., 45%) or a maximum win rate (e.g.,75%). The user engagement metric may include a repeat gameplay metric(e.g., a number or a proportion of user accounts represented by theplayer model who play the game at least once every three days) or agameplay duration (e.g., between 45 minutes and 2 hours).

At operation 2008, the game server determines whether the set of metricscorresponds to a predefined range. If the set of metrics corresponds tothe predefined range, the method 2000 continues to operation 2010. Ifthe set of metrics does not correspond to the predefined range, themethod 2000 continues to operation 2012.

At operation 2010, upon determining that the set of metrics correspondsto the predefined range, the game server stores the set of game contentitems for transmission to a client device associated with the playermodel. For example, the game server may store the set of game contentitems in a data repository. The data repository is accessible, via anetwork, by the client device. The client device may read the set ofgame content items from the data repository when the client device hasaccess to the network or when the client device has access to a low-costor high-speed (e.g., WiFi, rather than cellular) network. Afteroperation 2010, the method 2000 ends.

At operation 2012, upon determining that the set of metrics does notcorrespond to the predefined range, the game server determines whetheranother stopping condition is reached. The other stopping condition mayinclude one or more of: (i) at least n adjustments have occurred wherethe set of metrics has not gotten closer to the predefined range, wheren is a positive integer greater than or equal to two, and (ii) theadjusting (operation 2014), the forecasting (operation 2004), and/or thecomputing (operation 2006) operations have been repeated at least apredefined number of times (e.g., at least 1000 times) or over apredefined time period (e.g., the method 2000 has been implemented forover three hours). If the other stopping condition is reached, themethod 2000 ends. If the other stopping condition is not reached, themethod 2000 continues to operation 2014.

At operation 2014, upon determining that the other stopping condition isnot reached and that the set of metrics does not correspond to thepredefined range, the game server adjusts the set of game content items.To adjust the set of game content items, the game server may leverage ahyperparameter search based on the predefined range and the set of gamecontent items. After operation 2014, the method 2000 returns tooperation 2004 to re-forecast the sequence of user actions with theadjusted content items, and, at operation 2006, to re-compute the set ofmetrics based on the re-forecasted sequence of user actions.

Traversal Graph Analysis and Automated Psychology Prediction

As introduced above with reference to the example of FIG. 1-FIG. 6 (andexpanded on briefly in FIG. 3, FIG. 6, FIG. 11, and FIG. 12), one aspectof the disclosure relates to methods, systems, and techniques foranalyzing player traversal behavior (as opposed to exclusively gameplaybehavior) and to provide automated estimation or inference aboutpsychological aspect of player behavior, such as motivation for gameengagement and/or emotional states at different stages of interaction.The results of such analysis are in some examples included in aparametric player model as previously discussed, and thus automaticallyuses player motivations or emotional state to inform player clusteringand/or customization of game content.

Various features and functionalities according to this aspect of thedisclosure are described below by way of example with reference to FIG.21-FIG. 24B, which is for clarity and ease of reference described asbeing implemented by system 100 described previously with reference toFIG. 1. Note that, although various aspects relating to traversalbehavior analysis are described in the examples that follow as beingincorporated in a system that employs, in combination, the various otheraspects of this disclosure, these techniques can in other examples beemployed separately from at least some of the other techniques discussedherein. Moreover, the disclosed use of behavior data to infer userpsychology is not limited to game applications. These techniques are inother example employed to significant benefit for various types ofonline facilities and services, e.g., in content surfacing or suggestionon media content streaming services, social media platforms, onlineshopping, and the like.

These techniques are described below with reference to an example methodillustrated by diagram 2100 in FIG. 21. Aspects of player clustering andcustomization previously described (see, in particular, description withreference to FIG. 11 and FIG. 12) mainly discussed grouping playersbased on their gameplay behavior, which mostly describes how playersinteract with the game at a micro level during gameplay to complete alevel or event—e.g., how they play the game in terms of (in the commonexample game FIG. 2C) making words, using power-ups, etc. At ameta-level of behavior is the selection and sequencing of optionalinteractions or parts of the game applications that are performed orvisited. Such game interaction and sequential accessing of differentparts or functionalities provided by the game application is furtherreferenced as game traversal behavior or a player's journeys within thegame (each journey reference, e.g., to the sequence of actions in asingle session). At a higher level of abstraction, are psychologicalfeatures of player interaction with the game, such as the player'ssubjective motivation for accessing the application or performingcertain actions. This aspect of the disclosure provides techniques forinferring such player motivations and/or emotional states based onanalysis of their respective game traversal behavior.

Turning now to FIG. 21, therein is shown a flow diagram 2100 thatschematically illustrates an example method for modeling traversalbehavior and generating custom content based on analysis of the modeledbehavior. First, the way a user moves around the app and the order thatthey visit different aspects (i.e., their game traversal behavior) ismodeled as a directed graph (i.e., a data structure comprising nodesconnected by directional edges), thus building a graph database 2128(FIG. 21). In this example, each session is modeled by building, atoperation 2102, a respective traversal graph. In FIG. 22, exampletraversal graph 2200 schematically illustrates the graph structureemployed in this example for representing a single respective gameinteraction session, or journey. Note that the graph structure in thisexample for the session traversal graph 2200 includes a player nodeconnected by a respective edge to a session node, which is in turnconnected by respective edges to a successive series of step nodes. Eachstep node in turn connects to a single respective action node 2202selected from a predefined set of traversal or journey actions that areselected for tracking and/or inferring one or more psychologicalfeatures or states of users.

Additionally, each of the screens that the user passes through (e.g.,each action node 2202) is in some examples also annotated with anoperator's inference of a user's motivation or action. For example,collecting daily login rewards is inferred to be part of a “habit” basedmotivation, seeing a “win” screen is part of a challenge motivation, andso forth. As will be seen in what follows, a Neural Net approach canthen be used to group players together based at least in part on the waythat they flow through the game (i.e., their game traversal behavior),as captured by telemetry and subsequently sideloaded into the graphdatabase 2128. A benefit of using the graph database 2128 is that theaction data and the relationship between the action data are both ofsignificance. The graph database 2128, in this example employing thegraph structure of traversal graph 2200, enables the player modelingengine 120 to traverse the player's journeys or any sub-journeys moreefficiently than in a traditional relational database like SQLdatabases.

With the player traversal data being stored as directed graphs in thegraph database 2128, the player player modeling engine 120 is able todraw a player journey for any given player. The term “player journey” isin this example understood to mean a temporal sequence of player actionsduring a session, with each of the player actions selected from apredefined set of interactions with the game application, the set ofinteractions including at least some non-gameplay actions. An example ofa player journey 2302 associated with example Player 1 is shown in FIG.23A. Note that the player journey 2302 is here represented as a lineargraph limited to a sequence of respective actions 2304 with respectivedirectional edges connecting immediately adjacent actions 2304 in thesequence. The data extracted from the graph database 2128 and fedfurther downstream to other components in the method of FIG. 22, canrepresent respective player journeys 2302 in non-graph data structures.For example, each player journey 2302 is in some examples represented bya data structure comprising a series of separated action identifiers(e.g., a label or a numerical identifier), with the position of eachaction 2304 in the respective player journey 2302 being indicated by theposition of the corresponding identifier in the data structure.

To enable automated analysis of game traversal actions using a machinelearning model, the action labels are to be converted to numericalvalues. Simple natural number representation is avoided for implyingordinal relationship in the actions. An existing method to convertcategorical data (e.g., labels) to numerical data is “One-Hot Encoding”,where each label is converted to a vector containing a list of binaryvalues 0 and 1, each vector having a predefined number of values, withone of the values being 1 and the remainder of the values being 0. Theordinal position of each value is mapped to a respective predefinedattribute or parameter, in this instance being mapped to a respectivepredefined game action.

Suppose, for example a predefined set of 1000 distinct game actions, a1000×1000 diagonal matrix can be constructed to represent the wholeaction feature space, where each game action is represented by a vector(column) of length 1000 with values 0 and 1. Each vector will haveexactly one 1 and the rest of the values to be 0. The 1000×1000 matrixin referred to as the “One-Hot Embedding Matrix”, which is used toprovide a mapping for converting a game action to its correspondingrepresentation vector. For example, by the use of the One-Hot EmbeddingMatrix, a [Collected Rewards] action (e.g., FIG. 23A) can be convertedto be represented as [1,0,0,0,0, . . . 0], and [Won a Level] as[0,1,0,0,0,0, . . . 0].

This disclosure provides for a technique to generate a significantlydenser and low-dimensional vector representation for each of the gameactions, e.g., by the use of a Neural Network. This technique isreferred to herein as action embedding. For action embedding in thisexample, the 1000×1000 embedding matrix in the previous example isconverted to a much denser 1000×200 action embedding matrix with valuesranging from 0 to 1. Each action will be represented by a vector oflength 200 instead of 1000, thus providing a new 200×1000 actionembedding matrix. In the example of action embedding matrix 2306 in FIG.23C below, the action vectors are arranged as respective columns of thematrix 2306, with the rows defining respective embedding features.

This approach greatly reduces the computational complexity compared tousing the one-hot embedding. In addition, the action embedding matrix ismachine-learned by using relationships among the actions drawn from thehistorical player journey data, including which sets of actions tendedto occur together, and in what order. This has the useful result thatactions performed in analogous sequential relationships in a playerjourneys are represented by analogous action embedding vectors. Forexample, in the example action embedding matrix 2306 of FIG. 23C, itwill be seen that the [Purchased Hints Powerup] action and [Used HintsPowerup] action have more similar representation vectors (represented byrespective columns of matrix 2306) compared to the other two actions'vectors shown. Conventional one-hot encoding is agnostic of and silentto such relational similarities.

An example technique for deriving such an action embedding matrix usingplayer journey data is briefly described below with reference to exampleaction embedding matrix 2306 using example player journey 2302, beforereturning to describing use of this technique in the example method ofFIG. 21.

Step 1: First, player journey data is accessed. In this example, playerjourney 2302 of FIG. 23A is taken as example data.

Step 2: Discard the terminal action 2304 (that is, the last action), touse the pre-terminal actions 2304 (i.e., the actions immediately topredict the omitted action 2304. Thus, in this example, remove theaction [Used Hints Powerup] from the journey, then employed for traininga machine-learning model to use the rest of the actions in the journeyprior to [Used Hints Powerup] to predict that the next action is [UsedHints Powerup], as per the following steps.

Step 3: Define a candidate embedding matrix for each action 2304. Inthis prediction model, each action 2304 is first converted to areal-numbered vector by using a respective mapping function E, and usethis new set of vectors to predict what the next action in the journeyis [Used Hints Powerup]. The values for the candidate matrix and vectorscan be chosen arbitrarily.

Step 4: The candidate action embedding vectors for respective precedingactions 2304 of the player journey 2302, derived from the candidateembedding matrix E for the identified actions 2304, are then fed astraining input to a Neural Net Model (see example NN Model 2308 in FIG.23B) The mapping matrix E is the parameter that that is thus being fitto the NN Model 2308 to maximize the probability of predicting thecorrect the next action to be [Used Hints Powerup]. Differently viewed,the NN Model 2308 is trained providing a set of inputs (candidate actionvectors) and a labeled output (the actual next action in the playerjourney 2302).

Step 5: As shown schematically in FIG. 23B, iterating through thisprocess for many player journeys 2302 (in this example, through everyaction in every available player journey extracted from the graphdatabase 2128) provides an optimized mapping matrix 2306 (indicated asE_(opt) in FIG. 23B) that serves to predict each action in a givenplayer journey with high accuracy. This optimal mapping matrix 2306 isthen used as Action Embedding Matrix that for mapping each action to adense representation vector. Note, for example, the composition ofexample action embedding matrix 2306 in FIG. 23C, whose columns providerespective optimized action embedding vectors for the predefined set ofgame actions to be used for estimating associated player emotionalstates and/or motivations.

Returning now to FIG. 21, it will be seen that the method furthercomprises, at operation 2104, retrieving a superset of player journeysfor use as training data. In this example, the superset includes everyaction in every available player journey in the graph database 2128. Atoperation 2106, the action embedding vectors are optimized using the NNModel 2308, as per the scheme of FIG. 23B. At operation 2108, the actionembedding matrix is compiled from the optimized action embedding vectors(see, e.g., example embedding matrix 2306 of FIG. 23C).

Having the Action Embedding Matrix to map each in-game action to a denseand low-dimensional vector, a recurrent neural network (RNN) is thenconstructed based on player journey data, and this player journey RNN isthereafter used to predict, from one or more player journeys 2302 of aplayer, a label of interest associated with the player. In this example,the label of interest indicates one or more values for a psychologicalfeature of the player suggested by the respective player journey. e.g.,a particular emotional state or a particular motivation.

To this end, the example method of FIG. 21 further comprises, operation2124, retrieving a subset of player journeys from the graph database2128 for use as a training set for an example and, at operation 2126,assigning respective labels with respect to the relevant psychologicalfeature to respective items in the player journeys. In instances wheresuch psychological features are to be estimated for input journeys(e.g., to estimate a player motivations for a journey, as in the exampleof FIG. 24B) labels are mapped to journeys. The Many-to-One playerjourney described below with reference to FIG. 24B is most useful whenattempting to generate a single score/label to summarize a givenjourney. Such a many-to-one model serves to analyze the player'smotivation to play the game. In this scenario, the label being producedin this model (and assigned to each player journey 2302 in the trainingset, at operation 2126) is in this example one the following fourmotivating factors, which respectively inform custom configuration ofgame levels as discussed previously with respect to automated contentgeneration based on a parametric player model:

-   -   Improvement: building skill; overcoming challenges; feeling        mastery over a system.    -   Overcoming: Feeling smart: figuring out a puzzle; figuring out        the best strategies for a puzzle type. To sustain this        puzzle-solving pleasure, game designers would ideally vary the        way levels are structured, so players can feel smart more often,        from figuring out the best way to play for that particular        level/game configuration.    -   Habit: Following routine, grind—To sustain this pleasure,        players should be rewarded for engagement: they should feel like        they are making progress if they show up everyday. E.g., tricky        match 3 levels tend to eventually let you through by virtue of        the RNG if you play enough times.    -   Novelty: Experiencing novelty; desire to experience new levels,        challenges, or features.

In instances where psychological features are to be estimated forrespective actions (e.g., to estimate a player emotional state forrespective actions, as in the example of FIG. 24A) labels are mapped toactions. In such case, a Many-to-Many player journey model such as thatdescribed below with reference to FIG. 24A can be used to translate asequence of actions into a sequence of labels. One use case of thismodel is to simulate the player's emotional changes throughout the givenjourney, where the sequence of predicted labels here is a sequence ofemotional states, ranging from ‘happy’. ‘frustrated’, ‘shocked’,‘annoyed’. ‘regret’ etc. This will give insightful information for thegenerator to do emotion engineering in the game and tweak the gamedesign to induce desirable emotions.

At operation 2110, each player journey in the training set is mapped,using the previously constructed action embedding matrix, to acorresponding sequence of action embedding vectors These aspects of themethod are again illustrated using the example player journey 2302 andthe example action embedding matrix 2306. As shown schematically in FIG.23C, each of the actions 2304 in player journey 2302 is, via actionembedding matrix 2306, converted to a respective representation vectoror action embedding vector 2310. For example, action [Collected Rewards]will be mapped to the vector e_(collect) with value [0.98, 0.21, . . . ,0.09]^(T), and action [Won a Level] will be mapped to the vector e_(win)with value [0.12, 0.42, . . . , 0.34]^(T), etc.

At operation 2112 (FIG. 21), the action embedding vectors are used totrain a recurrent neural network model (in this example, RNN 2402 inFIG. 24A and FIG. 24B) by feeding the action embedding vectors 2310sequentially based on their original temporal order in the playerjourney 2302, the RNN 2402 being trained to conform such inputs to thecorresponding assigned motivation/emotional state label (also referredto for short as a psych label). This RNN model allow the production of asingle player label of interest or a temporal sequence of labelsassociated with that player throughout the given journey, depending onthe choice of a many-to-many structure (FIG. 24A) or a many-to-onestructure (FIG. 24B).

By the use of a many-to-many player journey RNN model as shown in FIG.24A, the system 100 implementing these techniques in the example isenabled to use the temporal sequence of player journey data to provideinsights on the characteristics of a player, including psychologicalinsights or profiles. A player journey is translated into a sequence oflabels that summarizes the player's characteristics or psychologicalbehavior profile. For example, in an example in which the predictedlabel at each layer of the RNN model (e.g., at each action 2304)represents an emotional state, the method enables simulation orinference of the player's emotional change overtime throughout the givenjourney if the predicted label at each layer represents an emotionalstate.

The RNN model training (at operation 2112) in the many-to-many structureof FIG. 24A in this example proceeds as follow:

-   -   Step 1: Take the first action in the player journey [Collected        Rewards] and feed its embedding vector to construct the first        hidden layer of RNN 2402. Output a predicted label (Label 1)        using the information in Layer 1. The first output from RNN 2402        for each layer is a predicted probability distribution. e.g.,        [0.1, 0.6; 0.3], where each number represent the probability of        an emotional state (e.g., [Happy, Sad, Frustrated], and the sum        of these three numbers is 1 (equal to 100% probability). A        single label ‘Sad’ is then assigned as the motivation of this        journey because that is the psych parameter with the largest        probability value, 0.6.    -   Step 2: Use the action embedding vector 2310 for the second        action 2304 [Won a Level] together with compacted information        [a₁] passed from Layer 1 to construct a second hidden layer in        the neural network. Output a predicted label from Layer 2.        Unlike Layer 1, Layer 2 is constructed using inputs from two        sources: compacted information from Layer 1; and action vector        2310 from the player journey 2302.    -   Step 3: Repeat Step 2 for each but the last action, continuing        the same procedure to construct a new hidden neural network        layer at each action.    -   Last Step: Use the embedding vector 2310 for the last action        2304 [Used Hints Powerup] together with the information passed        from the previous layers to construct the last hidden layer in        the neural network, thus producing a predicted label from the        last layer.

The output from this example RNN 2402 is thus a sequence of labels[Label₁, Label₂, . . . , Label_(n)], in this example, e.g., [Sad,Frustrated, . . . , Sad].

Instead of (or in addition to) generating a temporal sequence of labelslike the emotional change for a given player journey, the system is insome examples configured to output a single prediction label at the verylast step, thus providing a many-to-one construction, as illustratedschematically by way of example in FIG. 24B. This is employed when asingle score/label is desired to summarize the entire journey. Forexample, if the primary motivation that brings players back to the gameis to be identified, the following RNN structure of FIG. 24B can beemployed by skipping outputting predicted labels in the intermediatehidden layers. Instead, compacted information is passed sequentiallyfrom layer to layer until the last or terminal layer, at which thepredicted label is output.

Analogously to the initial outputs of the intermediate layers in FIG.24A, the initial output of RNN 2402 in the many-to-one model of FIG. 24Bis, for each player, a predicted probability distribution, e.g., [0.1,0.6; 0.2; 0.1], where each number represent the probability of amotivation type [motiv1, motiv2, motiv3, motiv4], and the sum of thesefour numbers is 1 (equal to 100% probability). Then a single label‘motiv2’ is assigned as the motivation of the journey underconsideration, because the ‘motiv2’ label has the largest probabilityvalue of 0.6.

Returning now to FIG. 10, it will be seen that after the recurring NNhas been trained, as described, a respective latest player journey isretrieved, at operation 2122, from the graph database 2128 for eachplayer that is to be assessed. At operation 2114, each player journey ismapped to the action embedding matrix to extract a sequence of actionembedding vectors, as described previously with reference to FIG. 23C.The respective sequence of action embedding vectors for each playerjourney is then fed to the trained RNN 2402, which assigns a singlepsych label for each journey (FIG. 24B) or a sequence of psych labelsfor each player journey (FIG. 24A).

The result of this automated estimation or inference of the relevantaspects of player psychology can be used beneficially in a variety ofmechanisms. In some instances, the outputs are used for grouping orclustering players, or for assessment of game reception by a playerpopulation or sub-population. In this example, the results of theautomated evaluation is incorporated in respective parametric playermodels, as discussed at length previously with reference to FIG. 11 andFIG. 12.

Thus, at operation 2118, the estimated label output is ingested by theplayer modeling engine (see, e.g., FIG. 21, FIG. 11, and FIG. 12) andare folded into the parametric player models for the respective players.In the example of FIG. 24B, where respective journeys are mapped tolabels for the four motivation types of this example, the player modelincorporates the initial probability distribution values for therespective motion motivation types as respective parametric values for acorresponding four motivational parameters or dimensions of the playermodel. It will be appreciated that different schemes for parametrisingthe label output and incorporating it in a player model can be employed.These parametric values are then in some examples predefined as linkedparameters that inform automated selection of a correspondingconfigurable features of a game level, in a manner closely analogous tothat described with reference to FIG. 16-FIG. 18H above.

In the example of FIG. 24A, where each journey produces a sequence oflabels, the system in this example does an analysis that assigns aunique 0.0-1.0 number to each journey (like a lookup index). That indexgoes into the player model and is used by downstream systems to derivethe sequence, while encoding the sequence as a single number in themodel. In a particular example, such encoding comprises the followingsequence of operations:

-   -   1. Flip the sequence to read right to left (rather than left to        right). So [emo1, emo2, emo3]→[emo3, emo2, emo1];    -   2. Swap the emo-label for a numeral→[3,2,1]    -   3. Concatenate to a base-5 number→321    -   4. Transcode to base-10 number→86    -   5. Normalize based on the biggest base-5 number possible for        length 6 (444444→15624), 86/15624=0.00550425227854583

6. This is now an encoding that uniquely address the sequence [emo3,emo2, emo1] and that is be merged into the player model data as a newdimension.

It will be seen that benefits of this aspects of the disclosuresprovides for automated identification of otherwise indistinguishableplayer motivations and emotional responses to interaction with the game.The results of this powerful analysis is moreover parametrized andseamlessly incorporated into parametric player model systems, thusallowing for inferred psychological aspects of player behavior to informautomated player clustering and/or automated generation of customcontent. All of these benefits are achieved with minimal labor cost.

Recapitulation of Some Disclosed Examples

From the preceding description it will be seen that a number of exampleembodiments and combinations of example embodiments are disclosed. Somenumbered examples (Example 1, 2, 3, etc.) are provided below. These areprovided as examples only and do not limit the technology disclosedherein.

Example 1 is a method comprising: causing display on an operator deviceof a generator interface for a content generator configured for at leastpartly automated generation of configuration data for a game event in acomputer-implemented game, the game event comprising a plurality of gamelevels; via an event sizing mechanism provided by the generatorinterface, receiving operator input of an event size value thatquantifies the plurality of game levels which are together to form thegame event and for which respective configuration data is to begenerated; via a target input mechanism provided by the generatorinterface, receiving operator input defining, for each of the pluralityof game levels, a respective value for a target metric with respect togameplay performance in the game level; and causing automated generationby the content generator of configuration data for the game event suchthe plurality of game levels together forming the game event conform innumber to the event size value, respective configuration data for eachgame level being generated based at least in part on the respectivelycorresponding target metric value.

In Example 2, the subject matter of Example 1 includes, wherein thetarget input mechanism is a graph mechanism defining a target curve thatvisually represents the respective target metric values for theplurality of game levels, the respective target metric values beingseparately manipulable via operator interaction with the graph mechanismto change the target curve.

In Example 3, the subject matter of Examples 1-2 includes, wherein thegenerator interface further provides a cluster selection mechanismconfigured to receive operator input selecting one of a plurality of apredefined player clusters defining respective subsets of a global setof players of the game, the method including communicating to thecontent generator player model data with respect to the selected playercluster to cause the generation of the configuration data based at leastin part on the player model data in combination with the target metricvalues, the configuration data thus being customized for the selectedplayer cluster.

In Example 4, the subject matter of Example 3 includes, maintaining arespective player model for each player of the game, each player modelcomprising a multi-dimensional vector having a respective numericaldimension value for each of a predefined set of dimensions indicatingdifferent respective aspects of player behavior; combining therespective player models of those players together forming the selectedplayer cluster, thereby to generate a representative player model forthe selected cluster; communicating the representative player model forthe selected player cluster to the content generator for generation ofthe configuration data based at least in part on the representativeplayer model.

In Example 5, the subject matter of Examples 1-4 includes, wherein thetarget metric is a difficulty level of the respective game level.

In Example 6, the subject matter of Examples 1-5 includes, wherein thegenerator interface is provided by a game server system in communicationwith the operator device via a distributed computer network, wherein thegenerator interface is a web-based user interface.

In Example 7, the subject matter of Examples 1-6 includes, via a rulesselection mechanism provided by the generator interface, receivingoperator input selecting one of a plurality of different predefinedrulesets, each ruleset defining a set of computer-implementable gamerules; and communicating the selected ruleset from the generatorinterface to the content generator, thus causing configuration by thecontent generator of the game event for gameplay according to theselected ruleset.

In Example 8, the subject matter of Example 7 includes, displaying inthe generator interface a code editing mechanism configured to: displaythe selected ruleset as editable code; and enable operator editing ofthe selected ruleset to produce an edited ruleset, so that the contentgenerator generates the configuration data of the game event forgameplay according to the edited ruleset.

Example 9 is a system comprising: one or more computer processordevices; and memory storing instructions to configure the system, whenthe instructions are executed by the one or more computer processordevices, to perform operations comprising: causing display on anoperator device of a generator interface for a content generatorconfigured for at least partly automated generation of configurationdata for a game event in a computer-implemented game, the game eventcomprising a plurality of game levels; via an event sizing mechanismprovided by the generator interface, receiving operator input of anevent size value that quantifies the plurality of game levels which aretogether to form the game event and for which respective configurationdata is to be generated; via a target input mechanism provided by thegenerator interface, receiving operator input defining, for each of theplurality of game levels, a respective value for a target metric withrespect to gameplay performance in the game level; and causing automatedgeneration by the content generator of configuration data for the gameevent such the plurality of game levels together forming the game eventconform in number to the event size value, respective configuration datafor each game level being generated based at least in part on therespectively corresponding target metric value.

In Example 10, the subject matter of Example 9 includes, wherein thetarget input mechanism is a graph mechanism defining a target curve thatvisually represents the respective target metric values for theplurality of game levels, the respective target metric values beingseparately manipulable via operator interaction with the graph mechanismto change the target curve.

In Example 11, the subject matter of Examples 9-10 includes, wherein theinstructions further configure the system such that the generatorinterface further provides a cluster selection mechanism configured toreceive operator input selecting one of a plurality of a predefinedplayer clusters defining respective subsets of a global set of playersof the game, the system being configured to communicate to the contentgenerator player model data with respect to the selected player clusterto cause the generation of the configuration data based at least in parton the player model data in combination with the target metric values,the configuration data thus being customized for the selected playercluster.

In Example 12, the subject matter of Example 11 includes, wherein theinstructions further configure the system to perform operationscomprising: maintaining a respective player model for each player of thegame, each player model comprising a multi-dimensional vector having arespective numerical dimension value for each of a predefined set ofdimensions indicating different respective aspects of player behavior;combining the respective player models of those players together formingthe selected player cluster, thereby to generate a representative playermodel for the selected cluster; communicating the representative playermodel for the selected player cluster to the content generator forgeneration of the configuration data based at least in part on therepresentative player model.

In Example 13, the subject matter of Examples 9-12 includes, wherein thetarget metric is a difficulty level of the respective game level.

In Example 14, the subject matter of Examples 9-13 includes, wherein thegenerator interface is provided by a game server system in communicationwith the operator device via a distributed computer network, wherein thegenerator interface is a web-based user interface.

In Example 15, the subject matter of Examples 9-14 includes, wherein theinstructions further configure the system to perform operationscomprising: via a rules selection mechanism provided by the generatorinterface, receiving operator input selecting one of a plurality ofdifferent predefined rulesets, each ruleset defining a set ofcomputer-implementable game rules; and communicating the selectedruleset from the generator interface to the content generator, thuscausing configuration by the content generator of the game event forgameplay according to the selected ruleset.

In Example 16, the subject matter of Example 15 includes, wherein theinstructions further configure the system to display in the generatorinterface a code editing mechanism configured to: display the selectedruleset as editable code; and enable operator editing of the selectedruleset to produce an edited ruleset, so that the content generatorgenerates the configuration data of the game event for gameplayaccording to the edited ruleset.

Example 17 is a non-transitory computer-readable storage medium havingstored thereon instructions that, when executed by one or more computerprocessor devices, cause the one or more computer processor devices toperform operations comprising: causing display on an operator device ofa generator interface for a content generator configured for at leastpartly automated generation of configuration data for a game event in acomputer-implemented game, the game event comprising a plurality of gamelevels; via an event sizing mechanism provided by the generatorinterface, receiving operator input of an event size value thatquantifies the plurality of game levels which are together to form thegame event and for which respective configuration data is to begenerated; via a target input mechanism provided by the generatorinterface, receiving operator input defining, for each of the pluralityof game levels, a respective value for a target metric with respect togameplay performance in the game level, and causing automated generationby the content generator of configuration data for the game event suchthe plurality of game levels together forming the game event conform innumber to the event size value, respective configuration data for eachgame level being generated based at least in part on the respectivelycorresponding target metric value.

In Example 18, the subject matter of Example 17 includes, wherein thetarget input mechanism is a graph mechanism defining a target curve thatvisually represents the respective target metric values for theplurality of game levels, the respective target metric values beingseparately manipulable via operator interaction with the graph mechanismto change the target curve.

In Example 19, the subject matter of Examples 17-18 includes, whereinthe generator interface further provides a cluster selection mechanismconfigured to receive operator input selecting one of a plurality of apredefined player clusters defining respective subsets of a global setof players of the game, the method including communicating to thecontent generator player model data with respect to the selected playercluster to cause the generation of the configuration data based at leastin part on the player model data in combination with the target metricvalues, the configuration data thus being customized for the selectedplayer cluster.

In Example 20, the subject matter of Example 19 includes, wherein theinstructions further configure the computer to: maintaining a respectiveplayer model for each player of the game, each player model comprising amulti-dimensional vector having a respective numerical dimension valuefor each of a predefined set of dimensions indicating differentrespective aspects of player behavior; combining the respective playermodels of those players together forming the selected player cluster,thereby to generate a representative player model for the selectedcluster; communicating the representative player model for the selectedplayer cluster to the content generator for generation of theconfiguration data based at least in part on the representative playermodel.

Example 21 is a method comprising: populating a multidimensional playermodel representing gameplay behavior in a computer-implemented game, theplayer model comprising a multi-value set of parametric valuescorresponding to a predefined parameter set defined by multiple gameplayparameters, each parametric value being a numerical value representingplayer behavior for a respectively corresponding gameplay parameter inthe parameter set; and in an at least partly automated procedure usingthe player model, customizing game content of the game for gameplayconsistent with the player model.

In Example 22, the subject matter of Example 21 includes, accessinghistorical gameplay data for multiple players of the game; and based onthe historical gameplay data, compiling a respective player model foreach of the multiple players, wherein the customizing of the gamecontent is based at least in part on the player models of the multipleplayers.

In Example 23, the subject matter of Example 22 includes, wherein eachparametric value is a real number, so that the set of parametric valuesfor each player model represent a datapoint in a continuous parameterspace defined by the multiple gameplay parameters as respectivedimensions.

In Example 24, the subject matter of Examples 22-23 includes, accessingplayer model data comprising respective player models for a first set ofplayers; in an automated procedure that is performed using one or morecomputer processor devices and that is based on the player model data,identifying a plurality of player clusters that form respective subsetsof the first set of players; and configuring customized game content forat least a particular one of the plurality of player clusters.

In Example 25, the subject matter of Example 24 includes, wherein theidentifying of the plurality of player clusters comprises: calculatingdistances, in a multidimensional parameter space whose dimensions aredefined by the predefined parameter set, between datapoints in theparameter space represented by the respective player models of the firstset of players; and identifying respective player clusters based onproximity clustering of the datapoints based on the calculateddistances.

In Example 26, the subject matter of Examples 24-25 includes, based onthe respective player models of the particular player cluster, compilinga representative player model for the particular player cluster, therepresentative player model defined by a respective set of parametricvalues for the predefined parameter set.

In Example 27, the subject matter of Example 26 includes, wherein therepresentative player model for the particular player cluster iscompiled by calculating a respective parametric value for each of thegameplay parameters based on respective parametric values of thecorresponding gameplay parameters in the player models of the cluster.

In Example 28, the subject matter of Examples 26-27 includes, whereinthe customizing of game content comprises, in an at least partlyautomated procedure, generating configuration data for a unit ofgameplay based at least in part on the representative player model, suchthat different configuration data is generated in different instances ofcustomization based on different respective representative playermodels.

In Example 29, the subject matter of Examples 26-28 includes, whereinthe customizing of game content comprises, based at least in part on therepresentative player model, performing automated evaluation ofconfiguration data for a unit of gameplay.

In Example 30, the subject matter of Example 29 includes, wherein theautomated evaluation comprises: simulating gameplay of the unit ofgameplay by according to the representative player model, and comparinga performance metric for the simulated gameplay to a predefined targetmetric.

Example 31 is a system comprising: one or more computer processordevices; and memory storing instructions to configure the system, whenthe instructions are executed by the one or more computer processordevices, to perform operations comprising: populating a multidimensionalplayer model representing gameplay behavior in a computer-implementedgame, the player model comprising a multi-value set of parametric valuescorresponding to a predefined parameter set defined by multiple gameplayparameters, each parametric value being a numerical value representingplayer behavior for a respectively corresponding gameplay parameter inthe parameter set; and in an at least partly automated procedure usingthe player model, customizing game content of the game for gameplayconsistent with the player model.

In Example 32, the subject matter of Example 31 includes, wherein theinstructions further configure the system to: access historical gameplaydata for multiple players of the game; and based on the historicalgameplay data, compile a respective player model for each of themultiple players, wherein the customizing of the game content is basedat least in part on the player models of the multiple players.

In Example 33, the subject matter of Example 32 includes, wherein eachparametric value is a real number, so that the set of parametric valuesfor each player model represent a datapoint in a continuous parameterspace defined by the multiple gameplay parameters as respectivedimensions.

In Example 34, the subject matter of Examples 32-33 includes, whereinthe instructions further configure the system to: access player modeldata comprising respective player models for a first set of players;identify a plurality of player clusters that form respective subsets ofthe first set of players; and configure customized game content for atleast a particular one of the plurality of player clusters.

In Example 35, the subject matter of Example 34 includes, wherein theinstructions further configure the system to: calculate distances, in amultidimensional parameter space whose dimensions are defined by thepredefined parameter set, between datapoints in the parameter spacerepresented by the respective player models of the first set of players;and identify respective player clusters based on proximity clustering ofthe datapoints based on the calculated distances.

In Example 36, the subject matter of Examples 34-35 includes, whereinthe instructions further configure the system to: based on therespective player models of the particular player cluster, compile arepresentative player model for the particular player cluster, therepresentative player model defined by a respective set of parametricvalues for the predefined parameter set.

In Example 37, the subject matter of Example 36 includes, wherein theinstructions further configure the system to compile the representativeplayer model for the particular player cluster by calculating arespective parametric value for each of the gameplay parameters based onrespective parametric values of the corresponding gameplay parameters inthe player models of the cluster.

In Example 38, the subject matter of Examples 36-37 includes, whereinthe instructions further configure the system to perform customize gamecontent by, in an at least partly automated procedure, generatingconfiguration data for a unit of gameplay based at least in part on therepresentative player model, such that different configuration data isgenerated in different instances of customization based on differentrespective representative player models.

In Example 39, the subject matter of Examples 36-38 includes, whereinthe instructions further configure the system to perform, based at leastin part on the representative player model, automated evaluation ofconfiguration data for a unit of gameplay.

Example 40 is a non-transitory computer-readable storage medium havingstored thereon instructions that, when executed by one or more computerprocessor devices, cause the one or more computer processor devices toperform operations comprising: accessing gameplay information forgameplay behavior of a target set of players for which custom gamecontent is to be generated, the gameplay information comprising aplurality of parametric values respectively corresponding to apredefined plurality of parameters for gameplay in acomputer-implemented game; and based at least in part on the gameplayinformation, performing an automated content generation procedure thatproduces configuration data defining respective custom values for aplurality of configurable features of a unit of gameplay, wherein thecontent generation procedure comprises a sequence of configurationstages in which the configuration data is progressively populated withcustom values for respective configurable features of the unit ofgameplay.

Example 41 is a method comprising: accessing gameplay information forgameplay behavior of a target set of players for which custom gamecontent is to be generated, the gameplay information comprising aplurality of parametric values respectively corresponding to apredefined plurality of parameters for gameplay in acomputer-implemented game; and in an automated process that is performedusing one or more computer processors configured therefor and that isbased at least in part on the gameplay information, performing anautomated content generation procedure that produces configuration datadefining respective custom values for a plurality of configurablefeatures of a unit of gameplay, wherein the content generation procedurecomprises a sequence of configuration stages in which the configurationdata is progressively populated with custom values for respectiveconfigurable features of the unit of gameplay.

In Example 42, the subject matter of Example 41 includes, wherein eachof the configuration stages comprises: receiving a current version of apredefined data structure for the configuration data; based at least onthe respective parametric value in the gameplay information for apredefined linked parameter, determining a custom value for a particularone of the plurality of configurable features of the unit of gameplay;and incorporating the custom value for the particular configurablefeature in the data structure, thereby updating the current version ofthe data structure such that it includes the custom value for theparticular configurable feature.

In Example 43, the subject matter of Example 42 includes, wherein, inone or more of the configuration stages, determination of the customvalue for the respective configurable feature is based at least in parton the respective custom values incorporated in the current version ofthe data structure in one or more preceding configuration stages.

In Example 44, the subject matter of Examples 42-43 includes, wherein,in at least one of the configuration stages, determination of the customvalue for the respective configurable feature is a randomized procedurebiased or constrained by a seed value derived from at least one linkedparametric value from the gameplay information.

In Example 45, the subject matter of Examples 42-44 includes, whereinthe determining of the respective custom value for one or more of theconfiguration stages comprises: using a non-linear response functiondefining a seed value for the respective configuration stage as afunction of the respective linked parametric value, determining a seedvalue specific to the configuration stage; and determining the customvalue for the respective configurable feature based on the seed value.

In Example 46, the subject matter of Example 45 includes, wherein theresponse function for at least one of the configuration stages definesthe respective seed value as a function of two or more respective linkedparametric values selected from the plurality of parameters of thegameplay information.

In Example 47, the subject matter of Examples 41-46 includes, whereinthe gameplay information comprises a player model that defines a vectorin a multidimensional parameter space defined by multiple gameplayparameters as coordinate dimensions, the vector being represented by acorresponding set of parametric values generated based on historicalgameplay by the target set of players for the respective gameplayparameters.

In Example 48, the subject matter of Examples 41-47 includes, whereinthe unit of gameplay is a game level, the configuration data defining alevel generation file consumable by a game engine to implement the gamelevel on a client device.

In Example 49, the subject matter of Examples 41-48 includes, whereinthe plurality of parametric values of the gameplay information arenormalized to a common value range.

Example 50 is a system comprising: one or more computer processordevices; and memory storing instructions to configure the system, whenthe instructions are executed by the one or more computer processordevices, to perform operations comprising: accessing gameplayinformation for gameplay behavior of a target set of players for whichcustom game content is to be generated, the gameplay informationcomprising a plurality of parametric values respectively correspondingto a predefined plurality of parameters for gameplay in acomputer-implemented game; and based at least in part on the gameplayinformation, performing an automated content generation procedure thatproduces configuration data define respective custom values for aplurality of configurable features of a unit of gameplay, wherein thecontent generation procedure comprises a sequence of configurationstages in which the configuration data is progressively populated withcustom values for respective configurable features of the unit ofgameplay.

In Example 51, the subject matter of Example 50 includes, wherein theinstructions further configure the system to perform the contentgeneration procedure such that each of the configuration stagescomprises: receiving a current version of a predefined data structurefor the configuration data; based at least on the respective parametricvalue in the gameplay information for a predefined linked parameter,determining a custom value for a particular one of the plurality ofconfigurable features of the unit of gameplay; and incorporating thecustom value for the particular configurable feature in the datastructure, thereby updating the current version of the data structuresuch that it includes the custom value for the particular configurablefeature.

In Example 52, the subject matter of Example 51 includes, wherein theinstructions further configure the system to determine, in one or moreof the configuration stages, the custom value for the respectiveconfigurable feature based at least in part on the respective customvalues incorporated in the current version of the data structure in oneor more precede configuration stages.

In Example 53, the subject matter of Examples 51-52 includes, whereinthe instructions further configure the system to, wherein, in at leastone of the configuration stages, determination of the custom value forthe respective configurable feature is a randomized procedure biased orconstrained by a seed value derived from at least one linked parametricvalue from the gameplay information.

In Example 54, the subject matter of Examples 51-53 includes, whereinthe instructions further configure the system to, wherein thedetermining of the respective custom value for one or more of theconfiguration stages comprises: using a non-linear response functiondefining a seed value for the respective configuration stage as afunction of the respective linked parametric value, determine a seedvalue specific to the configuration stage; and determine the customvalue for the respective configurable feature based on the seed value.

In Example 55, the subject matter of Example 54 includes, wherein theresponse function for at least one of the configuration stages definesthe respective seed value as a function of two or more respective linkedparametric values selected from the plurality of parameters of thegameplay information.

In Example 56, the subject matter of Examples 50-55 includes, whereinthe gameplay information comprises a player model that defines a vectorin a multidimensional parameter space defined by multiple gameplayparameters as coordinate dimensions, the vector being represented by acorresponding set of parametric values generated based on historicalgameplay by the target set of players for the respective gameplayparameters.

In Example 57, the subject matter of Examples 50-56 includes, whereinthe unit of gameplay is a game level, the configuration data defining alevel generation file consumable by a game engine to implement the gamelevel on a client device.

In Example 58, the subject matter of Examples 50-57 includes, whereinthe instructions further configure the system to normalize the pluralityof parametric values of the gameplay information to a common valuerange.

Example 59 is a non-transitory computer-readable storage medium havingstored thereon instructions that, when executed by one or more computerprocessor devices, cause the one or more computer processor devices toperform operations comprising: accessing gameplay information forgameplay behavior of a target set of players for which custom gamecontent is to be generated, the gameplay information comprising aplurality of parametric values respectively corresponding to apredefined plurality of parameters for gameplay in acomputer-implemented game; and based at least in part on the gameplayinformation, performing an automated content generation procedure thatproduces configuration data defining respective custom values for aplurality of configurable features of a unit of gameplay, wherein thecontent generation procedure comprises a sequence of configurationstages in which the configuration data is progressively populated withcustom values for respective configurable features of the unit ofgameplay.

In Example 60, the subject matter of Example 59 includes, wherein eachof the configuration stages comprises: receiving a current version of apredefined data structure for the configuration data, based at least onthe respective parametric value in the gameplay information for apredefined linked parameter, determining a custom value for a particularone of the plurality of configurable features of the unit of gameplay;and incorporating the custom value for the particular configurablefeature in the data structure, thereby updating the current version ofthe data structure such that it includes the custom value for theparticular configurable feature.

Example 61 is a method comprising: receiving behavior data for multipleplayers of a computer-implemented game, the behavior data indicatinginteraction journeys by respective players within the game, eachinteraction journey comprising a sequence of actions selected from apredefined set of actions and including one or more non-gameplayactions; extracting, from the behavior data, training data thatcomprises a subset of the interaction journeys in the behavior data;assigning to each interaction journey in the training data a respectivepsychological label indicating one of a predefined set of label valuespertaining to a psychological feature of player experience, therebyproviding labeled training data; using the labeled training data,training a neural network model (NN model) for label predictionresponsive to input of respective action sequences, thereby providing atrained NN model; and using the trained NN model, producing apsychological label prediction for a player based on a particularinteraction journey by the player.

In Example 62, the subject matter of Example 61 includes, based on thebehavior data, compiling graph data in which each interaction journey isrepresented as directed graph structure in which each action in thecorresponding sequence of actions is represented as a respective actionnode; and storing the graph data in a graph database.

In Example 63, the subject matter of Example 62 includes, wherein eachgraph structure in the graph database models a single respectiveinteraction session of an associated player with a game application.

In Example 64, the subject matter of Examples 61-63 includes, based onthe behavior data, building an action embedding matrix that maps eachrespective action of the predefined set of actions to a correspondingaction embedding vector; using the action embedding matrix, converting aplurality of actions in a given action sequence to corresponding actionembedding vectors; and providing the resultant plurality of actionembedding vectors as inputs to the NN model for the given actionsequence in at least one of: the training of the NN model, or labelestimation for the given action sequence.

In Example 65, the subject matter of Example 64 includes, wherein thebuilding of the action embedding matrix comprises: extracting multipleaction sequences from the behavior data; and using the multiple actionsequences provided to an embedding NN model, optimizing a candidateembedding matrix to predict as output a terminal action of each actionsequence responsive to input comprising a plurality of pre-terminalactions in the action sequence.

In Example 66, the subject matter of Examples 61-65 includes, whereinthe NN model is configured to produce a single respective psychologicallabel prediction for each respective sequence of actions.

In Example 67, the subject matter of Examples 61-66 includes, whereinthe psychological feature to which the predefined set of label valuespertain is player motivation for gameplay.

In Example 68, the subject matter of Examples 61-67 includes, whereinthe NN model is configured to produce a sequence of psychological labelpredictions for each respective sequence of actions.

In Example 69, the subject matter of Examples 61-68 includes, whereinthe psychological feature to which the predefined set of label valuespertain is an emotional state of the player.

In Example 70, the subject matter of Examples 61-69 includes,incorporating the psychological label prediction into a parametricplayer model; and in an automated procedure, generating custom gamecontent based at least in part on the psychological label predictionincorporated in the parametric player model.

Example 71 is a system comprising: one or more computer processordevices; and memory storing instructions to configure the system, whenthe instructions are executed by the one or more computer processordevices, to perform operations comprising: receiving behavior data formultiple players of a computer-implemented game, the behavior dataindicating interaction journeys by respective players within the game,each interaction journey comprising a sequence of actions selected froma predefined set of actions and including one or more non-gameplayactions; extracting, from the behavior data, training data thatcomprises a subset of the interaction journeys in the behavior data;responsive to operator input, assigning to each interaction journey inthe training data a respective psychological label indicating one of apredefined set of label values pertaining to a psychological feature ofplayer experience, thereby providing labeled training data; using thelabeled training data, training a neural network model (NN model) forlabel prediction responsive to input of respective action sequences,thereby providing a trained NN model; and using the trained NN model,producing a psychological label prediction for a player based on aparticular interaction journey by the player.

In Example 72, the subject matter of Example 71 includes, wherein theinstructions further configure the one or more computer processordevices to, based on the behavior data, compile graph data in which eachinteraction journey is represented as directed graph structure in whicheach action in the corresponding sequence of actions is represented as arespective action node; and store the graph data in a graph database.

In Example 73, the subject matter of Example 72 includes, wherein eachgraph structure in the graph database models a single respectiveinteraction session of an associated player with a game application.

In Example 74, the subject matter of Examples 71-73 includes, whereinthe instructions further configure the one or more computer processordevices to perform operations comprising: based on the behavior data,building an action embedding matrix that maps each respective action ofthe predefined set of actions to a corresponding action embeddingvector; using the action embedding matrix, converting a plurality ofactions in a given action sequence to corresponding action embeddingvectors; and providing the resultant plurality of action embeddingvectors as inputs to the NN model for the given action sequence in atleast one of: the training of the NN model; or label estimation for thegiven action sequence.

In Example 75, the subject matter of Example 74 includes, wherein thebuilding of the action embedding matrix comprises: extracting multipleaction sequences from the behavior data; and using the multiple actionsequences provided to an embedding NN model, optimizing a candidateembing matrix to predict as output a terminal action of each actionsequence responsive to input comprising a plurality of pre-terminalactions in the action sequence.

In Example 76, the subject matter of Examples 71-75 includes, whereinthe NN model is configured to produce a single respective psychologicallabel prediction for each respective sequence of actions.

In Example 77, the subject matter of Examples 71-76 includes, whereinthe psychological feature to which the predefined set of label valuespertain is player motivation for gameplay.

In Example 78, the subject matter of Examples 71-77 includes, whereinthe NN model is configured to produce a sequence of psychological labelpredictions for each respective sequence of actions.

In Example 79, the subject matter of Examples 71-78 includes, whereinthe psychological feature to which the predefined set of label valuespertain is an emotional state of the player.

Example 80 is a non-transitory computer-readable storage medium havingstored thereon instructions that, when executed by one or more computerprocessor devices, cause the one or more computer processor devices toperform operations comprising: receiving behavior data for multipleplayers of a computer-implemented game, the behavior data indicatinginteraction journeys by respective players within the game, eachinteraction journey comprising a sequence of actions selected from apredefined set of actions and including one or more non-gameplayactions; extracting, from the behavior data, training data thatcomprises a subset of the interaction journeys in the behavior data;responsive to operator input, assigning to each interaction journey inthe training data a respective psychological label indicating one of apredefined set of label values pertaining to a psychological feature ofplayer experience, thereby providing labeled training data; using thelabeled training data, training a neural network model (NN model) forlabel prediction responsive to input of respective action sequences,thereby providing a trained NN model; and using the trained NN model,producing a psychological label prediction for a player based on aparticular interaction journey by the player.

Example 81 is a method implemented at a game server, the methodcomprising: receiving, at the game server, data associated with gameplayof one or more user accounts, the one or more user accounts being usedfor playing a designated game; computing, for the one or more useraccounts, a player model representing previous in-game behavior of theone or more user accounts; identifying, based on the player model,configuration values for the designated game, wherein the configurationvalues represent data to specify operation of the designated game,wherein the configuration values represent at least one of a gamemechanics configuration and a game parameter configuration; causingtransmission of the configuration values to a client device, theconfiguration values causing the client device to adjust a game engineat the client device based on the previous in-game behavior of the oneor more user accounts.

In Example 82, the subject matter of Example 81 includes, wherein theconfiguration values represent one or more of: a player configuration,an event configuration, a level configuration, and a scoringconfiguration.

In Example 83, the subject matter of Examples 81-82 includes, whereinthe configuration values are transmitted to the client device in astandalone data payload over the air, the standalone data payload beingtransmitted directly from the game server to the client device, withoutpassing through an application store server.

In Example 84, the subject matter of Examples 81-83 includes, whereinthe client device stores the game engine, wherein the game engine isconfigurable using the configuration values, wherein the configurationvalues do not modify software of the game engine and are storedexternally to the software of the game engine.

In Example 85, the subject matter of Examples 81-84 includes, whereinthe player model comprises multiple dimensions, wherein identifying,based on the player model, the configuration values for the designatedgame comprises: clustering the player model into a group of playermodels using statistical methods, the group of player modelscorresponding to the configuration values.

In Example 86, the subject matter of Examples 81-85 includes, whereinthe previous in-game behavior of the one or more user accounts comprisesplaying the designated game.

In Example 87, the subject matter of Examples 81-86 includes, whereinthe game engine is for playing, at the client device, a plurality ofgames including the designated game.

Example 88 is a method implemented at a client device, the methodcomprising: storing, in a memory of the client device, a game engine forplaying a designated game; receiving, over a network, configurationvalues for the designated game, wherein the configuration valuesrepresent data to specify operation of the designated game, wherein theconfiguration values represent at least one of a game mechanicsconfiguration and a game parameter configuration; and adjustingoperation of the game engine based on the received configuration values,without modifying software of the game engine.

In Example 89, the subject matter of Example 88 includes, wherein theconfiguration values represent one or more of: a player configuration,an event configuration, a level configuration, and a scoringconfiguration.

In Example 90, the subject matter of Examples 88-89 includes, whereinthe configuration values are received, at the client device, in astandalone data payload over the air, the standalone data payload beingtransmitted directly from the game server to the client device, withoutpassing through an application store server.

In Example 91, the subject matter of Example 90 includes, receiving thegame engine from the application store server.

In Example 92, the subject matter of Examples 88-91 includes, storing,at the client device, the configuration values externally to the gameengine.

In Example 93, the subject matter of Examples 88-92 includes, whereinthe game engine for playing a plurality of games, including thedesignated game.

Example 94 is a non-transitory machine-readable medium storinginstructions which, when executed by a game server, cause the gameserver to perform operations comprising: receiving, at the game server,data associated with gameplay of one or more user accounts, the one ormore user accounts being used for playing a designated game; computing,for the one or more user accounts, a player model representing previousin-game behavior of the one or more user accounts; identifying, based onthe player model, configuration values for the designated game, whereinthe configuration values represent data to specify operation of thedesignated game, wherein the configuration values represent at least oneof a game mechanics configuration and a game parameter configuration;causing transmission of the configuration values to a client device, theconfiguration values causing the client device to adjust a game engineat the client device based on the previous in-game behavior of the oneor more user accounts.

In Example 95, the subject matter of Example 94 includes, wherein theconfiguration values represent one or more of: a player configuration,an event configuration, a level configuration, and a scoringconfiguration.

In Example 96, the subject matter of Examples 94-95 includes, whereinthe configuration values are transmitted to the client device in astandalone data payload over the air, the standalone data payload beingtransmitted directly from the game server to the client device, withoutpassing through an application store server.

In Example 97, the subject matter of Examples 94-96 includes, whereinthe client device stores the game engine, wherein the game engine isconfigurable using the configuration values, wherein the configurationvalues do not modify software of the game engine and are storedexternally to the software of the game engine.

In Example 98, the subject matter of Examples 94-97 includes, whereinthe player model comprises multiple dimensions, wherein identifying,based on the player model, the configuration values for the designatedgame comprises: clustering the player model into a group of playermodels using statistical methods, the group of player modelscorresponding to the configuration values.

In Example 99, the subject matter of Examples 94-98 includes, whereinthe previous in-game behavior of the one or more user accounts comprisesplaying the designated game.

In Example 100, the subject matter of Examples 94-99 includes, whereinthe game engine is for playing, at the client device, a plurality ofgames including the designated game.

Example 101 is a method implemented at a game server, the methodcomprising: accessing a player model representing a subset of players,the player model being generated based on previous in-game behavior ofthe subset of players while playing a computer-implemented game;accessing a set of interactive content items associated with the game;forecasting, using the player model, a sequence of user actions of thesubset of players during gameplay of the game, the sequence of useractions representing a prediction of user interaction with the set ofinteractive content items; computing, based on the forecasted sequenceof user actions and software-defined outcomes of the forecasted sequenceof user actions in the game, configuration values for the set ofinteractive content items; and causing execution of gameplay at a clientdevice associated with the player model, the gameplay being according tothe computed configuration values for the set of interactive contentitems.

In Example 102, the subject matter of Example 101 includes, whereinforecasting, using the player model, the user action leverages astatistical engine.

In Example 103, the subject matter of Example 102 includes, wherein thestatistical engine comprises a utility response curve or at least oneartificial neural network.

In Example 104, the subject matter of Examples 101-103 includes, whereinforecasting, using the player model, the user action comprises:presenting, to the player model, set of the content in an order in whichthe set of interactive content items is presented during gameplay of thegame.

In Example 105, the subject matter of Examples 101-104 includes,optimizing the configuration values based on at least one metric.

In Example 106, the subject matter of Example 105 includes, wherein theat least one metric comprises one or more of: a target win rate, arepeat gameplay metric, a gameplay duration, a game engagement metric,and a revenue metric.

In Example 107, the subject matter of Examples 101-106 includes, whereineach player of the subset of players represented by the player modelcorresponds to a data point in a multiple dimensional space, and whereinthe player model corresponds to a data point at a centroid of the subsetof players in the multiple dimensional space.

In Example 108, the subject matter of Examples 1-107 includes, whereinthe set of interactive content items prompts the sequence of useractions with the game.

Example 109 is a non-transitory machine-readable medium stornginstructions which, when executed by a game server, cause the gameserver to perform operations comprising: accessing a player modelrepresenting a subset of players, the player model being generated basedon previous in-game behavior of the subset of players while playing acomputer-implemented game; accessing a set of interactive content itemsassociated with the game; forecasting, using the player model, asequence of user actions of the subset of players during gameplay of thegame, the sequence of user actions representing a prediction of userinteraction with the set of interactive content items; computing, basedon the forecasted sequence of user actions and software-defined outcomesof the forecasted sequence of user actions in the game, configurationvalues for the set of interactive content items; and causing executionof gameplay at a client device associated with the player model, thegameplay being according to the computed configuration values for theset of interactive content items.

In Example 110, the subject matter of Example 109 includes, whereinforecasting, using the player model, the user action leverages astatistical engine.

In Example 111, the subject matter of Example 110 includes, wherein thestatistical engine comprises a utility response curve or at least oneartificial neural network.

In Example 112, the subject matter of Examples 109-111 includes, whereinforecasting, using the player model, the user action comprises:presenting, to the player model, set of the content in an order in whichthe set of interactive content items is presented during gameplay of thegame.

In Example 113, the subject matter of Examples 109-112 includes,optimizing the configuration values based on at least one metric.

In Example 114, the subject matter of Example 113 includes, wherein theat least one metric comprises one or more of: a target win rate, arepeat gameplay metric, a gameplay duration, a game engagement metric,and a revenue metric.

In Example 115, the subject matter of Examples 109-114 includes, whereineach player of the subset of players represented by the player modelcorresponds to a data point in a multiple dimensional space, and whereinthe player model corresponds to a data point at a centroid of the subsetof players in the multiple dimensional space.

In Example 116, the subject matter of Examples 109-115 includes, whereinthe set of interactive content items prompts the sequence of useractions with the game.

Example 117 is a game server comprising: processing circuitry; and amemory storing instructions which, when executed by the processingcircuitry, cause the processing circuitry to perform operationscomprising: accessing a player model representing a subset of players,the player model being generated based on previous in-game behavior ofthe subset of players while playing a computer-implemented game;accessing a set of interactive content items associated with the game;forecasting, using the player model, a sequence of user actions of thesubset of players during gameplay of the game, the sequence of useractions representing a prediction of user interaction with the set ofinteractive content items; computing, based on the forecasted sequenceof user actions and software-defined outcomes of the forecasted sequenceof user actions in the game, configuration values for the set ofinteractive content items, and causing execution of gameplay at a clientdevice associated with the player model, the gameplay being according tothe computed configuration values for the set of interactive contentitems.

In Example 118, the subject matter of Example 117 includes, whereinforecasting, using the player model, the user action leverages astatistical engine.

In Example 119, the subject matter of Example 118 includes, wherein thestatistical engine comprises a utility response curve or at least oneartificial neural network.

In Example 120, the subject matter of Examples 117-119 includes, whereinforecasting, using the player model, the user action comprises:presenting, to the player model, set of the content in an order in whichthe set of interactive content items is presented during gameplay of thegame.

Example 121 is a method implemented at a game server, the methodcomprising: generating a set of game content items for a designatedgame, the set of game content items being customized for one or moreuser accounts based on numerical values from a player model representingthe one or more user accounts, the player model being generated based onprevious in-game behavior of the one or more user accounts while playingthe designated game; forecasting, using the player model, a sequence ofuser actions of the one or more user accounts during gameplay of thedesignated game with the generated set of game content items, thesequence of user actions representing a prediction of in-game userinteraction with the set of game content items; computing, based on theforecasted sequence of user actions and software-defined outcomes of theforecasted sequence of user actions in the designated game, a set ofmetrics associated with gameplay of the one or more user accountsrepresented by the player model in the designated game; and providing anoutput based on the set of metrics.

In Example 122, the subject matter of Example 121 includes, whereinproviding the output based on the set of metrics comprises: determiningwhether the set of metrics corresponds to a predefined range; and upondetermining that the set of metrics corresponds to the predefined range:storing the set of game content items for transmission to a clientdevice associated with the player model.

In Example 123, the subject matter of Example 122 includes, upondetermining that the set of metrics does not correspond to thepredefined range: adjusting the set of game content items;re-forecasting the sequence of user actions with the adjusted contentitems; and re-computing the set of metrics based on the re-forecastedsequence of user actions.

In Example 124, the subject matter of Example 123 includes, whereinadjusting the set of game content items leverages a hyperparametersearch based on the predefined range and the set of game content items.

In Example 125, the subject matter of Examples 123-124 includes,repeating the adjusting, the re-forecasting, and the re-computingoperations until at least one of: (i) the set of metrics corresponds tothe predefined range, (ii) at least n adjustments have occurred wherethe set of metrics has not gotten closer to the predefined range,wherein n is a positive integer greater than or equal to two, and (iii)the adjusting, the re-forecasting, and the re-computing operations havebeen repeated at least a predefined number of times or over a predefinedtime period.

In Example 126, the subject matter of Examples 122-125 includes, whereinstoring the set of game content items for transmission to the clientdevice associated with the player model comprises: storing the set ofgame content items in a data repository, the data repository beingaccessible, via a network, by the client device.

In Example 127, the subject matter of Examples 121-126 includes, whereinthe set of metrics comprises at least one of: a user engagement metric,a gameplay metric, and a revenue metric.

In Example 128, the subject matter of Example 127 includes, wherein thegameplay metric comprises a minimum win rate or a maximum win rate.

In Example 129, the subject matter of Examples 127-128 includes, whereinthe user engagement metric comprises a repeat gameplay metric or agameplay duration.

In Example 130, the subject matter of Examples 121-129 includes, whereinthe set of game content items comprises interactive content itemsprompting the user action during gameplay of the designated game.

In Example 131, the subject matter of Examples 121-130 includes, whereinthe set of game content items is generated based on at least the playermodel and previously existing game content items of the designated game.

In Example 132, the subject matter of Examples 121-131 includes, whereinthe set of game content items is associated with a new level of thedesignated game.

Example 133 is a non-transitory machine-readable medium storinginstructions which, when executed at a game server, cause the gameserver to perform operations comprising: generating a set of gamecontent items for a designated game, the set of game content items beingcustomized for one or more user accounts based on numerical values froma player model representing the one or more user accounts, the playermodel being generated based on previous in-game behavior of the one ormore user accounts while playing the designated game; forecasting, usingthe player model, a sequence of user actions of the one or more useraccounts during gameplay of the designated game with the generated setof game content items, the sequence of user actions representing aprediction of in-game user interaction with the set of game contentitems; computing, based on the forecasted sequence of user actions andsoftware-defined outcomes of the forecasted sequence of user actions inthe designated game, a set of metrics associated with gameplay of theone or more user accounts represented by the player model in thedesignated game; and providing an output based on the set of metrics.

In Example 134, the subject matter of Example 133 includes, whereinproviding the output based on the set of metrics comprises: determiningwhether the set of metrics corresponds to a predefined range, and upondetermining that the set of metrics corresponds to the predefined range:storing the set of game content items for transmission to a clientdevice associated with the player model.

In Example 135, the subject matter of Example 134 includes, theoperations further comprising: upon determining that the set of metricsdoes not correspond to the predefined range: adjusting the set of gamecontent items; re-forecasting the sequence of user actions with theadjusted content items; and re-computing the set of metrics based on there-forecasted sequence of user actions.

In Example 136, the subject matter of Example 135 includes, whereinadjusting the set of game content items leverages a hyperparametersearch based on the predefined range and the set of game content items.

In Example 137, the subject matter of Examples 135-136 includes, theoperations further comprising: repeating the adjusting, there-forecasting, and the re-computing operations until at least one of:(i) the set of metrics corresponds to the predefined range, (ii) atleast n adjustments have occurred where the set of metrics has notgotten closer to the predefined range, wherein n is a positive integergreater than or equal to two, and (iii) the adjusting, there-forecasting, and the re-computing operations have been repeated atleast a predefined number of times or over a predefined time period.

In Example 138, the subject matter of Examples 134-137 includes, whereinstoring the set of game content items for transmission to the clientdevice associated with the player model comprises: storing the set ofgame content items in a data repository, the data repository beingaccessible, via a network, by the client device.

Example 139 is a game server comprising: processing circuitry; and amemory storing instructions which, when executed by the processingcircuitry, cause the processing circuitry to perform operationscomprising: generating a set of game content items for a designatedgame, the set of game content items being customized for one or moreuser accounts based on numerical values from a player model representingthe one or more user accounts, the player model being generated based onprevious in-game behavior of the one or more user accounts while playingthe designated game; forecasting, using the player model, a sequence ofuser actions of the one or more user accounts during gameplay of thedesignated game with the generated set of game content items, thesequence of user actions representing a prediction of in-game userinteraction with the set of game content items, computing, based on theforecasted sequence of user actions and software-defined outcomes of theforecasted sequence of user actions in the designated game, a set ofmetrics associated with gameplay of the one or more user accountsrepresented by the player model in the designated game; and providing anoutput based on the set of metrics.

In Example 140, the subject matter of Example 139 includes, whereinproviding the output based on the set of metrics comprises: determiningwhether the set of metrics corresponds to a predefined range; and upondetermining that the set of metrics corresponds to the predefined range;storing the set of game content items for transmission to a clientdevice associated with the player model.

Example 141 is a method implemented at a client device, the methodcomprising: receiving compiled software for a game engine, the gameengine being for playing a designated game; receiving a game definitionfile for the designated game, the game definition file storing rules forthe designated game, wherein the game definition file defines the rulesusing non-compiled machine-readable code; and executing the game engineat the client device to enable playing of the designated game via theclient device, wherein executing the game engine comprises: accessingthe game definition file; and providing, via a user interface generatedon the client device, interactive gameplay of the designated gameaccording to the rules for the designated game defined by the gamedefinition file.

In Example 142, the subject matter of Example 141 includes, receiving anupdated game definition file for the designated game; and executing thegame engine at the client device to play the specified game, whereinexecuting the game engine comprises: accessing the updated gamedefinition file; and providing, via the user interface generated on theclient device, interactive gameplay of the designated game according tothe rules for the designated game defined by the game definition file.

In Example 143, the subject matter of Example 142 includes, wherein theupdated game definition file is received without receiving an updatedgame engine and without updating the game engine.

In Example 144, the subject matter of Examples 141-143 includes, whereinthe game definition file represents a formal first order predicate logicfor playing the designated game via the game engine.

In Example 145, the subject matter of Examples 141-144 includes, whereinthe interactive gameplay of the designated game comprises providing, viathe user interface, a graphical output and receiving, via the userinterface at the client device, a user input for taking a user action inthe designated game.

In Example 146, the subject matter of Examples 141-145 includes, whereinat least one rule from the rules for the designated game comprises alogical manipulation of a game state in response to a user action or acurrent game state.

In Example 147, the subject matter of Example 146 includes, wherein thelogical manipulation of the game state indicates how a character or acontent item is moved in the designated game or how points are assignedin the designated game.

In Example 148, the subject matter of Examples 141-147 includes,wherein: the compiled software for the game play engine is received, atthe client device, from an application store server; and the gamedefinition file is received, at the client device, from a game server.

In Example 149, the subject matter of Example 148 includes, wherein theapplication store server is separate and distinct from the game server.

In Example 150, the subject matter of Examples 141-149 includes, whereinat least a portion of the application store server is identical to atleast a portion of the game server.

In Example 151, the subject matter of Examples 141-150 includes, whereinthe game engine is for playing a plurality of games including thedesignated game.

Example 152 is a non-transitory machine-readable medium storing: a gamedefinition file for a designated game, the game definition file storingrules for the designated game, wherein the game definition file definesthe rules using non-compiled machine-readable code; and compiledsoftware for a game engine, the game engine being for playing thedesignated game, wherein the game engine enables interactive gameplay ofthe designated game via a graphical user interface (GUI) according tothe rules for the designated game defined by the game definition file.

In Example 153, the subject matter of Example 152 includes, wherein thegame definition file represents a formal first order predicate logic forplaying the designated game via the game engine.

In Example 154, the subject matter of Examples 152-153 includes, whereinthe interactive gameplay of the designated game comprises providing, viathe GUI, a graphical output and receiving, via the GUI, a user input fortaking a user action in the designated game.

Example 155 is a client device comprising: processing circuitry; and amemory storing instructions which, when executed by the processingcircuitry, cause the processing circuitry to perform operationscomprising: receiving compiled software for a game engine, the gameengine being for playing a designated game; receiving a game definitionfile for the designated game, the game definition file storing rules forthe designated game, wherein the game definition file defines the rulesusing non-compiled machine-readable code; and executing the game engineat the client device to enable playing of the designated game via theclient device, wherein executing the game engine comprises: accessingthe game definition file; and providing, via a user interface generatedon the client device, interactive gameplay of the designated gameaccording to the rules for the designated game defined by the gamedefinition file.

In Example 156, the subject matter of Example 155 includes, theoperations further comprising: receiving an updated game definition filefor the designated game; and executing the game engine at the clientdevice to play the specified game, wherein executing the game enginecomprises: accessing the updated game definition file; and providing,via the user interface generated on the client device, interactivegameplay of the designated game according to the rules for thedesignated game defined by the game definition file.

In Example 157, the subject matter of Example 156 includes, wherein theupdated game definition file is received without receiving an updatedgame engine and without updating the game engine.

In Example 158, the subject matter of Examples 155-157 includes, whereinthe game definition file represents a formal first order predicate logicfor playing the designated game via the game engine.

In Example 159, the subject matter of Examples 155-158 includes, whereinthe interactive gameplay of the designated game comprises providing, viathe user interface, a graphical output and receiving, via the userinterface at the client device, a user input for taking a user action inthe designated game.

In Example 160, the subject matter of Examples 155-159 includes, whereinat least one rule from the rules for the designated game comprises alogical manipulation of a game state in response to a user action or acurrent game state.

Example 161 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-160.

Example 162 is an apparatus comprising means to implement of any ofExamples 1-160.

Example 163 is a system to implement of any of Examples 1-160.

Example 164 is a method to implement of any of Examples 1-160.

Machine and Software Architecture

The systems, system components, methods, applications, and so forthdescribed in conjunction with FIG. 1-FIG. 24B are implemented in someexamples in the context of a machine and an associated softwarearchitecture. The sections below describe representative softwarearchitecture(s) and machine (e.g., hardware) architecture(s) that aresuitable for use with the disclosed examples.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines configured for particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internet of things,” while yet another combination produces a servercomputer for use within a cloud computing architecture. The software andhardware architectures presented here are example architectures forimplementing the disclosure, and are not exhaustive as to possiblearchitectures that can be employed for implementing the disclosure.

Software Architecture

FIG. 25 is a block diagram illustrating an example software architecture2506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 25 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 2506 may execute on hardwaresuch as a machine 2600 of FIG. 26 that includes, among other things,processors 2604, memory 2614, and I/O components 2618. A representativehardware layer 2552 is illustrated and can represent, for example, themachine 2600 of FIG. 26. The representative hardware layer 2552 includesa processing unit 2554 having associated executable instructions 2504.The executable instructions 2504 represent the executable instructionsof the software architecture 2506, including implementation of themethods, components, and so forth described herein. The hardware layer2552 also includes memory and/or storage modules memory/storage 2556,which also have the executable instructions 2504. The hardware layer2552 may also comprise other hardware 2558.

In the example architecture of FIG. 25, the software architecture 2506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 2506may include layers such as an operating system 2502, libraries 2520,frameworks/middleware 2518, applications 2516, and a presentation layer2514. Operationally, the applications 2516 and/or other componentswithin the layers may invoke application programming interface (API)calls 2508 through the software stack and receive a response in the formof messages 2508. The layers illustrated are representative in nature,and not all software architectures have all layers. For example, somemobile or special-purpose operating systems may not provide aframeworks/middleware 2518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 2502 may manage hardware resources and providecommon services. The operating system 2502 may include, for example, akernel 2522, services 2524, and drivers 2526. The kernel 2522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 2522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 2524 may provideother common services for the other software layers. The drivers 2526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 2526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 2520 provide a common infrastructure that is used by theapplications 2516 and/or other components and/or layers. The libraries2520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 2502 functionality (e.g., kernel 2522,services 2524, and/or drivers 2526). The libraries 2520 may includesystem libraries 2544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 2520 may include API libraries 2546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D graphic content on a display), database libraries (e.g., SQLite thatmay provide various relational database functions), web libraries (e.g.,WebKit that may provide web browsing functionality), and the like. Thelibraries 2520 may also include a wide variety of other libraries 2548to provide many other APIs to the applications 2516 and other softwarecomponents/modules.

The frameworks/middleware 2518 provides a higher-level commoninfrastructure that may be used by the applications 2516 and/or othersoftware components/modules. For example, the frameworks/middleware 2518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 2518 may provide a broad spectrum of other APIsthat may be utilized by the applications 2516 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 2502 or platform.

The applications 2516 include built-in applications 2538 and/orthird-party applications 2540. Examples of representative built-inapplications 2538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 2540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 2540 may invoke the API calls 2508 provided bythe mobile operating system (such as the operating system 2502) tofacilitate functionality described herein.

The applications 2516 may use built-in operating system 2502 functions(e.g., kernel 2522, services 2524, and/or drivers 2526), libraries 2520,and frameworks/middleware 2518 to create user interfaces to interactwith users of the system. Alternatively, or additionally, in somesystems interactions with a user may occur through a presentation layer,such as the presentation layer 2514. In these systems, theapplication/component “logic” can be separated from the aspects of theapplication/component that interact with a user.

Hardware Architecture

FIG. 26 is a block diagram illustrating components of a machine 2600,according to some examples, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically. FIG. 26 shows a diagrammatic representation of the machine2600 in the example form of a computer system, within which instructions2610 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 2600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 2610 may be used to implement modules or componentsdescribed herein. The instructions 2610 transform the general,non-programmed machine 2600 into a particular machine 2600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative examples, the machine 2600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 2600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 2600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 2610, sequentially or otherwise, that specify actions to betaken by the machine 2600. Further, while only a single machine 2600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 2610 to perform any one or more of the methodologiesdiscussed herein. In this respect, a game server as described in someexamples in some examples comprise an interconnected collection ofserver machines and/or computer devices providing one or more computerprocessors respectively.

The machine 2600 may include processors 2604, memory/storage 2606, andI/O components 2618, which may be configured to communicate with eachother such as via a bus 2602. The memory/storage 2606 may include amemory 2614, such as a main memory, or other memory storage, and astorage unit 2616, both accessible to the processors 2604 such as viathe bus 2602. The storage unit 2616 and memory 2614 store theinstructions 2610 embodying any one or more of the methodologies orfunctions described herein. The instructions 2610 may also reside,completely or partially, within the memory 2614, within the storage unit2616, within at least one of the processors 2604 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 2600. Accordingly, the memory 2614, thestorage unit 2616, and the memory of the processors 2604 are examples ofmachine-readable media. In some examples, the processors 2604 comprise anumber of distributed processors 2608-2612, each of which have access toassociated memories storing instructions 2610.

The I/O components 2618 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 2618 that are included in a particular machine 2600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 2618 may include many other components that are not shown inFIG. 26. The I/O components 2618 are grouped according to functionalitymerely for simplifying the following discussion, and the grouping is inno way limiting. In various examples, the I/O components 2618 mayinclude output components 2626 and input components 2628. The outputcomponents 2626 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 2628 may include alphanumeric inputcomponents (e.g., a keyboard, a touchscreen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, atouchscreen that provides location and/or force of touches or touchgestures, or other tactile input components), audio input components(e.g., a microphone), and the like.

In further examples, the I/O components 2618 may include biometriccomponents 2630, motion components 2634, environment components 2636, orposition components 2638 among a wide array of other components. Forexample, the biometric components 2630 may include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 2634 may include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environment components2636 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas sensors to detect concentrationsof hazardous gases for safety or to measure pollutants in theatmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 2638 may include location sensorcomponents (e.g., a Global Positioning System (GPS) receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 2618 may include communication components 2640operable to couple the machine 2600 to a network 2632 or devices 2620via a coupling 2624 and a coupling 2622 respectively. For example, thecommunication components 2640 may include a network interface componentor other suitable device to interface with the network 2632. In furtherexamples, the communication components 2640 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 2620 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUniversal Serial Bus (USB)).

Moreover, the communication components 2640 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 2640 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix. Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components2640, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fit signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible media to facilitate communication of such instructions.Instructions may be transmitted or received over the network using atransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistant (PDA), smart phone, tablet, ultra book, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronic system, game console, set-top box, or any other communicationdevice that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network, andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or anothertype of cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long-Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data-transfer technology.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., Erasable Programmable Read-Only Memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, application programming interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various examples, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware componentsof a computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware component that operates to perform certain operations asdescribed herein. A hardware component may also be implementedmechanically, electronically, or any suitable combination thereof. Forexample, a hardware component may include dedicated circuitry or logicthat is permanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application-SpecificIntegrated Circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering examples in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In examples in which multiple hardware componentsare configured or instantiated at different times, communicationsbetween such hardware components may be achieved, for example, throughthe storage and retrieval of information in memory structures to whichthe multiple hardware components have access. For example, one hardwarecomponent may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther hardware component may then, at a later time, access the memorydevice to retrieve and process the stored output. Hardware componentsmay also initiate communications with input or output devices, and canoperate on a resource (e.g., a collection of information). The variousoperations of example methods described herein may be performed, atleast partially, by one or more processors that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an application programming interface (API)). Theperformance of certain of the operations may be distributed among theprocessors, not only residing within a single machine, but deployedacross a number of machines. In some examples, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other examples, the processors or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application-SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC),or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated, unless that thecontext and/or logic clearly indicates otherwise. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the disclosed subject matter has been describedwith reference to specific examples, various modifications and changesmay be made to these examples without departing from the broader scopeof examples of the present disclosure.

The examples illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other examples may be used and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. The Detailed Description, therefore, isnot to be taken in a limiting sense, and the scope of various examplesis defined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

What is claimed is:
 1. A method comprising: receiving behavior data formultiple players of a computer-implemented game, the behavior dataindicating interaction journeys by respective players within the game,each interaction journey comprising a sequence of actions selected froma predefined set of actions; based on the behavior data, compilingbehavior graph data in which interaction journeys are represented asrespective directed graph structures in which each action in thecorresponding sequence of actions is represented as a respective actionnode; storing the behavior graph data in a graph database, accessinglabel data that indicates assigned associations between a plurality ofpsychological labels and the behavior graph data, each psychologicallabel pertaining to a respective psychological feature of playerexperience or player behavior; using the behavior graph data and theassociated label data, training an artificial neural network forautomated label prediction, thereby providing a trained neural network;and using the trained neural network, producing a psychological labelprediction for a player based on a particular interaction journey by theplayer.
 2. The method of claim 1, further comprising: extracting, fromthe behavior graph data, training data that comprises respective graphstructures for a subset of the interaction journeys in the behaviordata; and assigning to each interaction journey in the training data atleast one respective psychological label selected from a predefined setof psychological labels, thereby providing labeled training data, thetraining of the artificial neural network is performed using the labeledtraining data.
 3. The method of claim 2, wherein the artificial neuralnetwork comprises a neural network model (NN model), wherein saidtraining operation comprises training the NN model for label predictionresponsive to input of respective action sequences, thereby providing atrained NN model, said producing of the psychological label predictionbeing performed using the trained NN model.
 4. The method of claim 2,wherein each graph structure in the graph database models a singlerespective interaction session of an associated player with a gameapplication.
 5. The method of claim 3, further comprising: based on thebehavior data, building an action embedding matrix that maps eachrespective action of the predefined set of actions to a correspondingaction embedding vector; using the action embedding matrix, converting aplurality of actions in a given action sequence to corresponding actionembedding vectors; and providing the resultant plurality of actionembedding vectors as inputs to the NN model for the given actionsequence at least one of: the training of the NN model, and labelestimation for the given action sequence.
 6. The method of claim 5,wherein the building of the action embedding matrix comprises:extracting multiple action sequences from the behavior data; and usingthe multiple action sequences provided to an embedding NN model,optimizing a candidate embedding matrix to predict as output a terminalaction of each action sequence responsive to input comprising aplurality of pre-terminal actions in the action sequence.
 7. The methodof claim 1, wherein the artificial neural network is configured toproduce a single respective psychological label prediction for eachinteraction journey received as input for label prediction.
 8. Themethod of claim 1, wherein one or more of the plurality of psychologicallabels pertains to player motivation for gameplay.
 9. The method ofclaim 1, wherein the artificial neural network is configured to producea sequence of psychological label predictions for each respectiveinteraction journey received as input for label prediction.
 10. Themethod of claim 1, wherein one or more of the plurality of psychologicallabels pertains to an emotional state of the player.
 11. The method ofclaim 1, further comprising: incorporating the psychological labelprediction into a parametric player model; and in an automatedprocedure, generating custom game content based at least in part on thepsychological label prediction incorporated in the parametric playermodel.
 12. A system comprising: one or more computer processor devices;and memory storing instructions to configure the system, when theinstructions are executed by the one or more computer processor devices,to perform operations comprising: receiving behavior data for multipleplayers of a computer-implemented game, the behavior data indicatinginteraction journeys by respective players within the game, eachinteraction journey comprising a sequence of actions selected from apredefined set of actions; based on the behavior data, compilingbehavior graph data in which interaction journeys are represented asrespective directed graph structures in which each action in thecorresponding sequence of actions is represented as a respective actionnode; storing the behavior graph data in a graph database; accessinglabel data that indicates assigned associations between a plurality ofpsychological labels and the behavior graph data, each psychologicallabel pertaining to a respective psychological feature of playerexperience or player behavior; using the behavior graph data and theassociated label data, training an artificial neural network forautomated label prediction, thereby providing a trained neural network;and using the trained neural network, producing a psychological labelprediction for a player based on a particular interaction journey by theplayer.
 13. The system of claim 12, wherein the instructions furtherconfigure the one or more computer processor devices to performoperations comprising: extracting, from the behavior graph data,training data that comprises respective graph structures for a subset ofthe interaction journeys in the behavior data; and assigning to eachinteraction journey in the training data at least one respectivepsychological label selected from a predefined set of psychologicallabels, thereby providing labeled training data, the training of theartificial neural network is performed using the labeled training data.14. The system of claim 13, wherein the artificial neural networkcomprises a neural network model (NN model), and wherein said trainingoperation comprises training the NN model for label predictionresponsive to input of respective action sequences, thereby providing atrained NN model, said producing of the psychological label predictionbeing performed using the trained NN model.
 15. The system of claim 14,wherein the instructions further configure the one or more computerprocessor devices to perform operations comprising: based on thebehavior data, building an action embedding matrix that maps eachrespective action of the predefined set of actions to a correspondingaction embedding vector; using the action embedding matrix, converting aplurality of actions in a given action sequence to corresponding actionembedding vectors; and providing the resultant plurality of actionembedding vectors as inputs to the NN model for the given actionsequence at least one of: the training of the NN model, and labelestimation for the given action sequence.
 16. The system of claim 15,wherein the building of the action embedding matrix comprises:extracting multiple action sequences from the behavior data; and usingthe multiple action sequences provided to an embedding NN model,optimizing a candidate embedding matrix to predict as output a terminalaction of each action sequence responsive to input comprising aplurality of pre-terminal actions in the action sequence.
 17. The systemof claim 12, wherein the artificial neural network is configured toproduce a single respective psychological label prediction for eachinteraction journey received as input for label prediction.
 18. Thesystem of claim 12, wherein the artificial neural network is configuredto produce a sequence of psychological label predictions for eachrespective interaction journey received as input for label prediction.19. The system of claim 12, wherein the instructions further configurethe system to, wherein the instructions further configure the apparatusto: incorporating the psychological label prediction into a parametricplayer model; and in an automated procedure, generating custom gamecontent based at least in part on the psychological label predictionincorporated in the parametric player model.
 20. A non-transitorycomputer-readable storage medium having stored thereon instructionsthat, when executed by one or more computer processor devices, cause theone or more computer processor devices to perform operations comprising:receiving behavior data for multiple players of a computer-implementedgame, the behavior data indicating interaction journeys by respectiveplayers within the game, each interaction journey comprising a sequenceof actions selected from a predefined set of actions; based on thebehavior data, compiling behavior graph data in which interactionjourneys are represented as respective directed graph structures inwhich each action in the corresponding sequence of actions isrepresented as a respective action node; storing the behavior graph datain a graph database; accessing label data that indicates assignedassociations between a plurality of psychological labels and thebehavior graph data, each psychological label pertaining to a respectivepsychological feature of player experience or player behavior; using thebehavior graph data and the associated label data, training anartificial neural network for automated label prediction, therebyproviding a trained neural network, and using the trained neuralnetwork, producing a psychological label prediction for a player basedon a particular interaction journey by the player.